Publications
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Books and Chapters
2023 |
Krishna, P; Ellis, M J Control-oriented hybrid modeling framework for building thermal modeling Book Chapter Li, M (Ed.): Chapter 9, AIP Publishing, Melville, NY, 2023, ISBN: 978-0-7354-2572-9. @inbook{Krishna20239, title = {Control-oriented hybrid modeling framework for building thermal modeling}, author = {P. Krishna and M. J. Ellis}, editor = {M. Li}, url = {https://doi.org/10.1063/9780735425743_009 }, doi = {10.1063/9780735425743_009}, isbn = {978-0-7354-2572-9}, year = {2023}, date = {2023-02-01}, publisher = {AIP Publishing}, address = {Melville, NY}, chapter = {9}, series = {Energy Systems and Processes: Recent Advances in Design and Control}, abstract = {Model predictive control has a high potential of delivering superior operations of buildings compared to conventional approaches. However, the unavailability of a scalable building modeling approach has hampered the broad adoption of MPC in buildings. Building spaces have unmeasured time-varying heat gains that evolve on similar timescales as the relevant building dynamics, making building model identification/parameter estimation challenging. In this chapter, a hybrid modeling framework for building thermal modeling is developed. The modeling framework consists of a control-oriented thermal resistance-capacitance (RC) model for the building thermal dynamics, a data-driven model for forecasting the unmeasured time-varying heat gains from external sources, and a data-driven model for modeling the nonlinear dynamics of the cooling or heating rate of the heating, ventilation, and air conditioning equipment with respect to the temperature setpoint. Specialized training approaches are developed for parameter estimation of the thermal RC model parameters to minimize the parameter bias caused by the correlation between the known and unknown inputs. The modeling framework is applied to an illustrative building space to demonstrate the prediction accuracy of the trained hybrid model.}, type = {incollection}, keywords = {}, pubstate = {published}, tppubtype = {inbook} } Model predictive control has a high potential of delivering superior operations of buildings compared to conventional approaches. However, the unavailability of a scalable building modeling approach has hampered the broad adoption of MPC in buildings. Building spaces have unmeasured time-varying heat gains that evolve on similar timescales as the relevant building dynamics, making building model identification/parameter estimation challenging. In this chapter, a hybrid modeling framework for building thermal modeling is developed. The modeling framework consists of a control-oriented thermal resistance-capacitance (RC) model for the building thermal dynamics, a data-driven model for forecasting the unmeasured time-varying heat gains from external sources, and a data-driven model for modeling the nonlinear dynamics of the cooling or heating rate of the heating, ventilation, and air conditioning equipment with respect to the temperature setpoint. Specialized training approaches are developed for parameter estimation of the thermal RC model parameters to minimize the parameter bias caused by the correlation between the known and unknown inputs. The modeling framework is applied to an illustrative building space to demonstrate the prediction accuracy of the trained hybrid model. |
2017 |
Lao, L; Ellis, M; Christofides, P D Economic model predictive control of transport-reaction processes Book Chapter Rohani, S (Ed.): Chapter 13, pp. 545–586, Butterworth-Heinemann, Netherlands, 2017, ISBN: 978-0-08-101095-2. @inbook{Lao2017545, title = {Economic model predictive control of transport-reaction processes}, author = {L. Lao and M. Ellis and P. D. Christofides}, editor = {S. Rohani}, url = {https://www.sciencedirect.com/science/article/pii/B9780081010952000138}, doi = {10.1016/B978-0-08-101095-2.00013-8}, isbn = {978-0-08-101095-2}, year = {2017}, date = {2017-08-01}, pages = {545--586}, publisher = {Butterworth-Heinemann}, address = {Netherlands}, chapter = {13}, series = {Process Control, Coulson & Richardson Series of Chemical Engineering}, abstract = {Transport-reaction processes, which are typically described by parabolic or hyperbolic partial differential equations (PDEs), play an important role within the chemical process industries. Therefore, it is beneficial to develop feedback control techniques that operate transport-reaction processes in an economically optimal fashion in the presence of constraints in the process states and manipulated inputs. Economic model predictive control (EMPC) is a predictive control scheme that combines process economics and feedback control into an integrated framework with the potential of improving the closed-loop process economic performance compared to traditional control methodologies. In this work, we address two key issues in applying EMPC to transport-reaction processes. First, we focus on a system of nonlinear parabolic PDEs and propose a novel EMPC design integrating adaptive proper orthogonal decomposition (APOD) method with a high-order finite-difference method to handle state constraints. The computational efficiency and constraint handling properties of this scheme are evaluated using a tubular reactor example modeled by two nonlinear parabolic PDEs. Second, we formulate an EMPC system that accounts for both manipulated input and state constraints for a system of first-order hyperbolic PDEs. Various closed-loop simulation scenarios are presented to demonstrate the overall effectiveness of this EMPC scheme using a plug flow reactor example.}, type = {incollection}, keywords = {}, pubstate = {published}, tppubtype = {inbook} } Transport-reaction processes, which are typically described by parabolic or hyperbolic partial differential equations (PDEs), play an important role within the chemical process industries. Therefore, it is beneficial to develop feedback control techniques that operate transport-reaction processes in an economically optimal fashion in the presence of constraints in the process states and manipulated inputs. Economic model predictive control (EMPC) is a predictive control scheme that combines process economics and feedback control into an integrated framework with the potential of improving the closed-loop process economic performance compared to traditional control methodologies. In this work, we address two key issues in applying EMPC to transport-reaction processes. First, we focus on a system of nonlinear parabolic PDEs and propose a novel EMPC design integrating adaptive proper orthogonal decomposition (APOD) method with a high-order finite-difference method to handle state constraints. The computational efficiency and constraint handling properties of this scheme are evaluated using a tubular reactor example modeled by two nonlinear parabolic PDEs. Second, we formulate an EMPC system that accounts for both manipulated input and state constraints for a system of first-order hyperbolic PDEs. Various closed-loop simulation scenarios are presented to demonstrate the overall effectiveness of this EMPC scheme using a plug flow reactor example. |
2016 |
Ellis, M; Liu, J; Christofides, P D Economic Model Predictive Control: Theory, Formulations and Chemical Process Applications. Book Springer-Verlag, London, England, 2016. @book{Ellis2016d, title = {Economic Model Predictive Control: Theory, Formulations and Chemical Process Applications.}, author = {M. Ellis and J. Liu and P. D. Christofides}, year = {2016}, date = {2016-08-01}, publisher = {Springer-Verlag}, address = {London, England}, series = {Economic Model Predictive Control: Theory, For- mulations and Chemical Process Applications}, keywords = {}, pubstate = {published}, tppubtype = {book} } |
Journal Articles
2023 |
Narasimhan, S; El-Farra, N H; Ellis, M J A reachable set-based scheme for the detection of false data injection cyberattacks on dynamic processes Journal Article Digital Chemical Engineering, 7 , pp. 100100, 2023. @article{Narasimhan2023100100, title = {A reachable set-based scheme for the detection of false data injection cyberattacks on dynamic processes}, author = {S. Narasimhan and N. H. El-Farra and M. J. Ellis}, doi = {10.1016/j.dche.2023.100100}, year = {2023}, date = {2023-05-01}, journal = {Digital Chemical Engineering}, volume = {7}, pages = {100100}, abstract = {Recent cyberattacks targeting process control systems have demonstrated that reliance on information technology-based approaches alone to address cybersecurity needs is insufficient and that operational technology-based solutions are needed. An attack detection scheme that monitors process operation and determines the presence of an attack represents an operational technology-based approach. Attack detection schemes may be designed to monitor a process operated at or near its steady–state to account for the typical operation of chemical processes. However, transient operation may occur; for example, during process start-up and set–point changes. Detection schemes designed or tuned for steady-state operation may raise false alarms during transient process operation. In this work, we present a reachable set-based cyberattack detection scheme for monitoring processes during transient operation. Both additive and multiplicative false data injection attacks (FDIAs) that alter data communicated over the sensor–controller and controller–actuator communication links are considered. For the class of attacks considered, the detection scheme does not raise false alarms during transient operations. Conditions for classifying attacks based on the ability of the detection scheme to detect the attacks are presented. The application of the reachable set-based detection scheme is demonstrated using two illustrative processes under different FDIAs. For the FDIAs considered, their detectability with respect to the reachable set-based detection scheme is analyzed.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Recent cyberattacks targeting process control systems have demonstrated that reliance on information technology-based approaches alone to address cybersecurity needs is insufficient and that operational technology-based solutions are needed. An attack detection scheme that monitors process operation and determines the presence of an attack represents an operational technology-based approach. Attack detection schemes may be designed to monitor a process operated at or near its steady–state to account for the typical operation of chemical processes. However, transient operation may occur; for example, during process start-up and set–point changes. Detection schemes designed or tuned for steady-state operation may raise false alarms during transient process operation. In this work, we present a reachable set-based cyberattack detection scheme for monitoring processes during transient operation. Both additive and multiplicative false data injection attacks (FDIAs) that alter data communicated over the sensor–controller and controller–actuator communication links are considered. For the class of attacks considered, the detection scheme does not raise false alarms during transient operations. Conditions for classifying attacks based on the ability of the detection scheme to detect the attacks are presented. The application of the reachable set-based detection scheme is demonstrated using two illustrative processes under different FDIAs. For the FDIAs considered, their detectability with respect to the reachable set-based detection scheme is analyzed. |
2022 |
Narasimhan, S; El-Farra, N H; Ellis, M J A control-switching approach for cyberattack detection in process systems with minimal false alarms Journal Article AIChE Journal, 68 , pp. e17875, 2022. @article{Narasimhan2022, title = {A control-switching approach for cyberattack detection in process systems with minimal false alarms}, author = {S. Narasimhan and N. H. El-Farra and M. J. Ellis}, doi = {10.1002/aic.17875}, year = {2022}, date = {2022-12-01}, journal = {AIChE Journal}, volume = {68}, pages = {e17875}, abstract = {The frequency of cyberattacks against process control systems has increased in recent years. This work considers multiplicative false-data injection attacks involving the multiplication of the data communicated over the sensor-controller communication link by a factor. An active detection method utilizing switching between two control modes is developed to balance the trade-off between closed-loop performance and attack detectability. Under the first mode, the control parameters are selected using traditional control design criteria. Under the second mode, the control parameters are selected to enhance the attack detection capability. A switching condition is imposed to prevent false alarms that could be triggered by the transient response induced by control mode switching. This condition is incorporated into the active detection method to minimize false alarms. The active detection method is applied to illustrative process examples to demonstrate its ability to detect attacks and minimize false alarms.}, keywords = {}, pubstate = {published}, tppubtype = {article} } The frequency of cyberattacks against process control systems has increased in recent years. This work considers multiplicative false-data injection attacks involving the multiplication of the data communicated over the sensor-controller communication link by a factor. An active detection method utilizing switching between two control modes is developed to balance the trade-off between closed-loop performance and attack detectability. Under the first mode, the control parameters are selected using traditional control design criteria. Under the second mode, the control parameters are selected to enhance the attack detection capability. A switching condition is imposed to prevent false alarms that could be triggered by the transient response induced by control mode switching. This condition is incorporated into the active detection method to minimize false alarms. The active detection method is applied to illustrative process examples to demonstrate its ability to detect attacks and minimize false alarms. |
Chinde, V; Lin, Y; Ellis, M J Data-enabled predictive control for building HVAC systems Journal Article Journal of Dynamic Systems, Measurement, and Control, 144 , pp. 081001, 2022. @article{Chinde2022081001, title = {Data-enabled predictive control for building HVAC systems}, author = {V. Chinde and Y. Lin and M. J. Ellis}, url = {https://doi.org/10.1115/1.4054314}, doi = {10.1115/1.4054314}, year = {2022}, date = {2022-08-01}, journal = {Journal of Dynamic Systems, Measurement, and Control}, volume = {144}, pages = {081001}, abstract = {Model predictive control is widely used as a control technology for the computation of optimal control inputs of building heating, ventilating, and air conditioning (HVAC) systems. However, both the benefits and widespread adoption of model predictive control (MPC) are hindered by the effort of model creation, calibration, and accuracy of the predictions. In this paper, we apply the data-enabled predictive control (DeePC) algorithm for designing controls for building HVAC systems. The algorithm solely depends on input/output data from the system to predict future state trajectories without the need for system identification. The algorithm relies on the idea that a vector space of all input–output trajectories of a discrete-time linear time-invariant (LTI) system is spanned by time-shifts of a single measured trajectory, given the input signal is persistently exciting. Closed-loop simulations using EnergyPlus are performed to demonstrate the approach. The simulated building modeled in EnergyPlus is a modified commercial large office prototype building served by an air handling unit-variable air volume HVAC system. Temperature setpoints of zones are used as control variables to minimize the HVAC energy cost of the building considering a time-of-use electricity rate structure. Furthermore, sensitivity analysis is conducted to gain insights into the effect of parameter tuning on DeePC performance. Simulation results are used to illustrate the performance of the algorithm and compare the algorithm with model-based MPC and occupancy-based setpoint controller. Overall, DeePC achieves similar performance compared to MPC for lower engineering effort.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Model predictive control is widely used as a control technology for the computation of optimal control inputs of building heating, ventilating, and air conditioning (HVAC) systems. However, both the benefits and widespread adoption of model predictive control (MPC) are hindered by the effort of model creation, calibration, and accuracy of the predictions. In this paper, we apply the data-enabled predictive control (DeePC) algorithm for designing controls for building HVAC systems. The algorithm solely depends on input/output data from the system to predict future state trajectories without the need for system identification. The algorithm relies on the idea that a vector space of all input–output trajectories of a discrete-time linear time-invariant (LTI) system is spanned by time-shifts of a single measured trajectory, given the input signal is persistently exciting. Closed-loop simulations using EnergyPlus are performed to demonstrate the approach. The simulated building modeled in EnergyPlus is a modified commercial large office prototype building served by an air handling unit-variable air volume HVAC system. Temperature setpoints of zones are used as control variables to minimize the HVAC energy cost of the building considering a time-of-use electricity rate structure. Furthermore, sensitivity analysis is conducted to gain insights into the effect of parameter tuning on DeePC performance. Simulation results are used to illustrate the performance of the algorithm and compare the algorithm with model-based MPC and occupancy-based setpoint controller. Overall, DeePC achieves similar performance compared to MPC for lower engineering effort. |
Narasimhan, S; El-Farra, N H; Ellis, M J Active multiplicative cyberattack detection utilizing controller switching for process systems Journal Article Journal of Process Control, 116 , pp. 64-79, 2022. @article{Narasimhan202264, title = {Active multiplicative cyberattack detection utilizing controller switching for process systems}, author = {S. Narasimhan and N. H. El-Farra and M. J. Ellis}, doi = {10.1016/j.jprocont.2022.05.014}, year = {2022}, date = {2022-08-01}, journal = {Journal of Process Control}, volume = {116}, pages = {64-79}, abstract = {Multiplicative cyberattacks manipulating data over the process control system (PCS) communication links are cyberattacks that malicious agents may carry out against PCSs. These attacks are modeled by multiplying the data communicated over the link by a factor, and may be designed to be stealthy without extensive knowledge of process dynamics. The current work characterizes the relationship between the control system parameters, the closed-loop stability, and the detectability of a multiplicative sensor–controller communication link attack with respect to a class of residual-based detection schemes. The analysis reveals that control system parameters may be selected to aid in attack detection. Specifically, control system parameters, called attack-sensitive parameters, may be selected so that the closed-loop process is stable under attack-free operation and is destabilized by a cyberattack, rendering the attack detectable. With the attack-sensitive parameters, however, the attack-free closed-loop process performance may be worse than that with parameters selected based on standard design criteria. To address the potential trade-off between attack-free closed-loop performance and attack detectability, a novel active attack detection methodology utilizing control system parameter switching is developed. The control system switches between the nominal parameters (selected based on standard design criteria) and the attack-sensitive parameters to improve attack detection capabilities while avoiding substantial degradation in the attack-free closed-loop performance. The active detection methodology is applied to an illustrative chemical process example and shown to enhance the attack detection capabilities of two representative residual-based detection schemes}, keywords = {}, pubstate = {published}, tppubtype = {article} } Multiplicative cyberattacks manipulating data over the process control system (PCS) communication links are cyberattacks that malicious agents may carry out against PCSs. These attacks are modeled by multiplying the data communicated over the link by a factor, and may be designed to be stealthy without extensive knowledge of process dynamics. The current work characterizes the relationship between the control system parameters, the closed-loop stability, and the detectability of a multiplicative sensor–controller communication link attack with respect to a class of residual-based detection schemes. The analysis reveals that control system parameters may be selected to aid in attack detection. Specifically, control system parameters, called attack-sensitive parameters, may be selected so that the closed-loop process is stable under attack-free operation and is destabilized by a cyberattack, rendering the attack detectable. With the attack-sensitive parameters, however, the attack-free closed-loop process performance may be worse than that with parameters selected based on standard design criteria. To address the potential trade-off between attack-free closed-loop performance and attack detectability, a novel active attack detection methodology utilizing control system parameter switching is developed. The control system switches between the nominal parameters (selected based on standard design criteria) and the attack-sensitive parameters to improve attack detection capabilities while avoiding substantial degradation in the attack-free closed-loop performance. The active detection methodology is applied to an illustrative chemical process example and shown to enhance the attack detection capabilities of two representative residual-based detection schemes |
Narasimhan, S; El-Farra, N H; Ellis, M J Detectability-based controller design screening for processes under multiplicative cyberattacks Journal Article AIChE Journal, 68 , pp. e17430, 2022. @article{Narasimhan2022e17430, title = {Detectability-based controller design screening for processes under multiplicative cyberattacks}, author = {S Narasimhan and N H El-Farra and M J Ellis}, doi = {10.1002/aic.17430}, year = {2022}, date = {2022-01-01}, journal = {AIChE Journal}, volume = {68}, pages = {e17430}, abstract = {Cyberattacks on process control systems (PCSs) may target communication links, compromising the data integrity. Cyberattack detection and mitigation are essential capabilities, since the consequences of a successful cyberattack on a PCS may be severe. While detectability may be viewed as a systems-theoretic property, cyberattack detectability in practice depends on the attack detection scheme used and the PCS design. This paper presents an approach for control parameter screening based on the detectability of sensor-controller communication link multiplicative attacks. First, a residual set-based condition for the undetectability of an attack is developed. A controller screening methodology aimed at identifying controller parameter choices that mask the detectability of an attack is presented. The proposed methodology can be used to incorporate the detectability of an attack as a criterion into conventional control design criteria (e.g., closed-loop stability and economic considerations). Finally, the application of the controller screening methodology is demonstrated using two illustrative examples.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Cyberattacks on process control systems (PCSs) may target communication links, compromising the data integrity. Cyberattack detection and mitigation are essential capabilities, since the consequences of a successful cyberattack on a PCS may be severe. While detectability may be viewed as a systems-theoretic property, cyberattack detectability in practice depends on the attack detection scheme used and the PCS design. This paper presents an approach for control parameter screening based on the detectability of sensor-controller communication link multiplicative attacks. First, a residual set-based condition for the undetectability of an attack is developed. A controller screening methodology aimed at identifying controller parameter choices that mask the detectability of an attack is presented. The proposed methodology can be used to incorporate the detectability of an attack as a criterion into conventional control design criteria (e.g., closed-loop stability and economic considerations). Finally, the application of the controller screening methodology is demonstrated using two illustrative examples. |
2020 |
Redeker, D C; Jiang, D Y; Kullar, J S; Leung, V; Palazoglu, A; Ellis, M J Comment on "CO2 utilization feasibility study: Dimethyl carbonate direct synthesis process with dehydration reactive distillation'' Journal Article Industrial & Engineering Chemistry Research, 34 (59), pp. 15387-15389, 2020. @article{Redeker202015387, title = {Comment on "CO2 utilization feasibility study: Dimethyl carbonate direct synthesis process with dehydration reactive distillation''}, author = {D C Redeker and D Y Jiang and J S Kullar and V Leung and A Palazoglu and M J Ellis}, url = {https://doi.org/10.1021/acs.iecr.0c03372}, doi = {10.1021/acs.iecr.0c03372}, year = {2020}, date = {2020-12-31}, journal = {Industrial & Engineering Chemistry Research}, volume = {34}, number = {59}, pages = {15387-15389}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Ellis, M J; Chinde, V An encoder–decoder LSTM-based EMPC framework applied to a building HVAC system Journal Article Chemical Engineering Research and Design, 160 , pp. 508–520, 2020, ISSN: 0263-8762. @article{Ellis2020508, title = {An encoder–decoder LSTM-based EMPC framework applied to a building HVAC system}, author = {M J Ellis and V Chinde}, url = {http://www.sciencedirect.com/science/article/pii/S0263876220302690}, doi = {https://doi.org/10.1016/j.cherd.2020.06.008}, issn = {0263-8762}, year = {2020}, date = {2020-01-01}, journal = {Chemical Engineering Research and Design}, volume = {160}, pages = {508--520}, abstract = {Numerous studies have demonstrated the benefit of economic model predictive control (EMPC) applied to building heating, ventilation, and air conditioning (HVAC) systems. However, the construction and training of predictive models for building HVAC systems are widely recognized as a key technological barrier preventing large-scale adoption of EMPC for buildings. In this work, an encoder–decoder long short-term memory-based EMPC framework is developed. The key advantage of the approach is that a model may be automatically generated from a list of inputs and outputs. From the definition of inputs and outputs, the constructed model may be trained and automatically embedded into the EMPC framework for real-time estimation and control. The overall end-to-end EMPC framework from model training to on-line estimation and control are described. To this end, the encoder–decoder model provides a natural framework for state estimation (encoder), which is required to provide an initial condition for the predictive model of EMPC (decoder). Closed-loop simulations using EnergyPlus are performed to demonstrate the approach. The simulated closed-loop system consists of a building zone from a multi-zone building, which is served by an air handling unit-variable air volume HVAC system. For the HVAC example considered, the trained encoder–decoder model can predict the indoor air temperature and HVAC sensible cooling rate of a building zone over a two-day horizon with high accuracy. Considering a time-of-use electric rate structure, the EMPC, which manipulates the zone temperature setpoint, can reduce the HVAC power consumption cost relative to keeping the zone temperature setpoint at its maximum value (i.e., minimum energy approach).}, keywords = {}, pubstate = {published}, tppubtype = {article} } Numerous studies have demonstrated the benefit of economic model predictive control (EMPC) applied to building heating, ventilation, and air conditioning (HVAC) systems. However, the construction and training of predictive models for building HVAC systems are widely recognized as a key technological barrier preventing large-scale adoption of EMPC for buildings. In this work, an encoder–decoder long short-term memory-based EMPC framework is developed. The key advantage of the approach is that a model may be automatically generated from a list of inputs and outputs. From the definition of inputs and outputs, the constructed model may be trained and automatically embedded into the EMPC framework for real-time estimation and control. The overall end-to-end EMPC framework from model training to on-line estimation and control are described. To this end, the encoder–decoder model provides a natural framework for state estimation (encoder), which is required to provide an initial condition for the predictive model of EMPC (decoder). Closed-loop simulations using EnergyPlus are performed to demonstrate the approach. The simulated closed-loop system consists of a building zone from a multi-zone building, which is served by an air handling unit-variable air volume HVAC system. For the HVAC example considered, the trained encoder–decoder model can predict the indoor air temperature and HVAC sensible cooling rate of a building zone over a two-day horizon with high accuracy. Considering a time-of-use electric rate structure, the EMPC, which manipulates the zone temperature setpoint, can reduce the HVAC power consumption cost relative to keeping the zone temperature setpoint at its maximum value (i.e., minimum energy approach). |
2019 |
Patel, N R; Rawlings, J B; Ellis, M J; Wenzel, M J; Turney, R D Economic optimization of distributed embedded battery units for large-scale heating, ventilation, and air conditioning applications Journal Article AIChE Journal, 65 , pp. e16576, 2019. @article{Patel2019e16576, title = {Economic optimization of distributed embedded battery units for large-scale heating, ventilation, and air conditioning applications}, author = {N R Patel and J B Rawlings and M J Ellis and M J Wenzel and R D Turney}, url = {https://aiche.onlinelibrary.wiley.com/doi/abs/10.1002/aic.16576}, doi = {10.1002/aic.16576}, year = {2019}, date = {2019-01-01}, journal = {AIChE Journal}, volume = {65}, pages = {e16576}, abstract = {Energy costs of space heating and cooling systems can be significantly decreased using energy storage to shift the load from periods of high prices to periods of low prices. In this work, an economically optimal method of controlling large systems with distributed embedded batteries units is proposed. The control system with load shifting outperforms the conventional trade‐off curve between cost and occupant comfort. Remarks are also made about the economic viability of these systems moving forward.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Energy costs of space heating and cooling systems can be significantly decreased using energy storage to shift the load from periods of high prices to periods of low prices. In this work, an economically optimal method of controlling large systems with distributed embedded batteries units is proposed. The control system with load shifting outperforms the conventional trade‐off curve between cost and occupant comfort. Remarks are also made about the economic viability of these systems moving forward. |
Kumar, R; Jalving, J; Wenzel, M J; Ellis, M J; Elbsat, M N; Drees, K H; Zavala, V M Benchmarking stochastic and deterministic MPC: A case study in stationary battery systems Journal Article AIChE Journal, 65 , pp. e16551, 2019. @article{Kumar2019e16551, title = {Benchmarking stochastic and deterministic MPC: A case study in stationary battery systems}, author = {R Kumar and J Jalving and M J Wenzel and M J Ellis and M N Elbsat and K H Drees and V M Zavala}, url = {https://aiche.onlinelibrary.wiley.com/doi/abs/10.1002/aic.16551}, doi = {10.1002/aic.16551}, year = {2019}, date = {2019-01-01}, journal = {AIChE Journal}, volume = {65}, pages = {e16551}, abstract = {We present a computational framework that integrates forecasting, uncertainty quantification, and model predictive control (MPC) to benchmark the performance of deterministic and stochastic MPC. By means of a battery management case study, we illustrate how off‐the‐shelf deterministic MPC implementations can suffer significant losses in performance and constraint violations due to their inability to handle disturbances that cannot be adequately represented by mean (most likely) forecasts. We also show that adding constraint back‐off terms can help ameliorate these issues but this approach is ad hoc and does not provide performance guarantees. Stochastic MPC provides a more systematic framework to handle these issues by directly capturing uncertainty descriptions of a wide range of disturbances.}, keywords = {}, pubstate = {published}, tppubtype = {article} } We present a computational framework that integrates forecasting, uncertainty quantification, and model predictive control (MPC) to benchmark the performance of deterministic and stochastic MPC. By means of a battery management case study, we illustrate how off‐the‐shelf deterministic MPC implementations can suffer significant losses in performance and constraint violations due to their inability to handle disturbances that cannot be adequately represented by mean (most likely) forecasts. We also show that adding constraint back‐off terms can help ameliorate these issues but this approach is ad hoc and does not provide performance guarantees. Stochastic MPC provides a more systematic framework to handle these issues by directly capturing uncertainty descriptions of a wide range of disturbances. |
Kumar, R; Wenzel, M J; Ellis, M J; Elbsat, M N; Drees, K H; Zavala, V M Hierarchical MPC schemes for periodic systems using stochastic programming Journal Article Automatica, 107 , pp. 306–316, 2019, ISSN: 0005-1098. @article{Kumar2019306, title = {Hierarchical MPC schemes for periodic systems using stochastic programming}, author = {R Kumar and M J Wenzel and M J Ellis and M N Elbsat and K H Drees and V M Zavala}, url = {http://www.sciencedirect.com/science/article/pii/S0005109819302821}, doi = {https://doi.org/10.1016/j.automatica.2019.05.054}, issn = {0005-1098}, year = {2019}, date = {2019-01-01}, journal = {Automatica}, volume = {107}, pages = {306--316}, abstract = {We show that stochastic programming provides a framework to design hierarchical model predictive control (MPC) schemes for periodic systems. This is based on the observation that, if the state policy of an infinite-horizon problem is periodic, the problem can be cast as a stochastic program (SP). This reveals that it is possible to update periodic state targets by solving a retroactive optimization problem that progressively accumulates historical data. Moreover, we show that the retroactive problem is a statistical approximation of the SP and thus delivers optimal targets in the long run. Notably, the computation of the optimal targets can be achieved without data forecasts. The SP setting also reveals that the retroactive problem can be seen as a high-level hierarchical layer that provides targets to guide a low-level MPC controller that operates over a short period at high time resolution. We derive a retroactive scheme tailored to linear systems by using cutting plane techniques and suggest strategies to handle nonlinear systems and to analyze stability properties.}, keywords = {}, pubstate = {published}, tppubtype = {article} } We show that stochastic programming provides a framework to design hierarchical model predictive control (MPC) schemes for periodic systems. This is based on the observation that, if the state policy of an infinite-horizon problem is periodic, the problem can be cast as a stochastic program (SP). This reveals that it is possible to update periodic state targets by solving a retroactive optimization problem that progressively accumulates historical data. Moreover, we show that the retroactive problem is a statistical approximation of the SP and thus delivers optimal targets in the long run. Notably, the computation of the optimal targets can be achieved without data forecasts. The SP setting also reveals that the retroactive problem can be seen as a high-level hierarchical layer that provides targets to guide a low-level MPC controller that operates over a short period at high time resolution. We derive a retroactive scheme tailored to linear systems by using cutting plane techniques and suggest strategies to handle nonlinear systems and to analyze stability properties. |
2018 |
Kumar, R; Wenzel, M J; Ellis, M J; ElBsat, M N; Drees, K H; Zavala, V M A stochastic model predictive control framework for stationary battery systems Journal Article IEEE Transactions on Power Systems, 33 , pp. 4397–4406, 2018, ISSN: 0885-8950. @article{Kumar20184397, title = {A stochastic model predictive control framework for stationary battery systems}, author = {R Kumar and M J Wenzel and M J Ellis and M N ElBsat and K H Drees and V M Zavala}, doi = {10.1109/TPWRS.2017.2789118}, issn = {0885-8950}, year = {2018}, date = {2018-01-01}, journal = {IEEE Transactions on Power Systems}, volume = {33}, pages = {4397--4406}, abstract = {A stochastic model predictive control (MPC) framework is presented to determine real-time commitments in energy and frequency regulation markets for a stationary battery while simultaneously mitigating long-term demand charges for an attached load. The control problem is multi-scale in nature and poses challenges on computational tractability of the stochastic program and of forecasting and uncertainty quantification (UQ) procedures. The framework deals with tractability of the stochastic program by using a discounting factor for long-term demand charges, while a Ledoit-Wolf covariance estimator is used to overcome UQ tractability issues. The performance of stochastic MPC is benchmarked against that of perfect information MPC and deterministic MPC for different prediction horizon lengths and demand charge discounting strategies. A case study using real load data for a typical university campus and price and regulation data from PJM is considered. It is found that stochastic MPC can recover 83% of the ideal value of the battery, which is defined as the expected savings obtained by operating the battery under perfect information MPC. In contrast, deterministic MPC can only recover 73% of this ideal value. It is also found that operating the battery under stochastic MPC improves the battery payback period by 12.1%, while operating it under perfect information improves it by 27.9%.}, keywords = {}, pubstate = {published}, tppubtype = {article} } A stochastic model predictive control (MPC) framework is presented to determine real-time commitments in energy and frequency regulation markets for a stationary battery while simultaneously mitigating long-term demand charges for an attached load. The control problem is multi-scale in nature and poses challenges on computational tractability of the stochastic program and of forecasting and uncertainty quantification (UQ) procedures. The framework deals with tractability of the stochastic program by using a discounting factor for long-term demand charges, while a Ledoit-Wolf covariance estimator is used to overcome UQ tractability issues. The performance of stochastic MPC is benchmarked against that of perfect information MPC and deterministic MPC for different prediction horizon lengths and demand charge discounting strategies. A case study using real load data for a typical university campus and price and regulation data from PJM is considered. It is found that stochastic MPC can recover 83% of the ideal value of the battery, which is defined as the expected savings obtained by operating the battery under perfect information MPC. In contrast, deterministic MPC can only recover 73% of this ideal value. It is also found that operating the battery under stochastic MPC improves the battery payback period by 12.1%, while operating it under perfect information improves it by 27.9%. |
2016 |
Ellis, M; Durand, H; Christofides, P D Elucidation of the role of constraints in economic model predictive control Journal Article Annual Reviews in Control, 41 , pp. 208–217, 2016, ISSN: 1367-5788. @article{Ellis2016208, title = {Elucidation of the role of constraints in economic model predictive control}, author = {M Ellis and H Durand and P D Christofides}, url = {http://www.sciencedirect.com/science/article/pii/S1367578816300074}, doi = {http://dx.doi.org/10.1016/j.arcontrol.2016.04.004}, issn = {1367-5788}, year = {2016}, date = {2016-01-01}, journal = {Annual Reviews in Control}, volume = {41}, pages = {208--217}, abstract = {Economic model predictive control (EMPC) is a predictive feedback control methodology that unifies economic optimization and control. EMPC uses a stage cost that reflects the process/system economics. In general, the stage cost used is not a quadratic stage cost like that typically used in standard tracking model predictive control. In this paper, a brief overview of EMPC methods is provided. In particular, the role of constraints imposed in the optimization problem of EMPC for feasibility, closed-loop stability, and closed-loop performance is explained. Three main types of constraints are considered including terminal equality constraints, terminal region constraints, and constraints designed via Lyapunov-based techniques. The paper closes with a well-known chemical engineering example (a non-isothermal CSTR with a second-order reaction) to illustrate the effectiveness of time-varying operation to improve closed-loop economic performance compared to steady-state operation and to demonstrate the impact of economically motivated constraints on optimal operation.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Economic model predictive control (EMPC) is a predictive feedback control methodology that unifies economic optimization and control. EMPC uses a stage cost that reflects the process/system economics. In general, the stage cost used is not a quadratic stage cost like that typically used in standard tracking model predictive control. In this paper, a brief overview of EMPC methods is provided. In particular, the role of constraints imposed in the optimization problem of EMPC for feasibility, closed-loop stability, and closed-loop performance is explained. Three main types of constraints are considered including terminal equality constraints, terminal region constraints, and constraints designed via Lyapunov-based techniques. The paper closes with a well-known chemical engineering example (a non-isothermal CSTR with a second-order reaction) to illustrate the effectiveness of time-varying operation to improve closed-loop economic performance compared to steady-state operation and to demonstrate the impact of economically motivated constraints on optimal operation. |
Durand, H; Ellis, M; Christofides, P D Economic model predictive control designs for input rate-of-change constraint handling and guaranteed economic performance Journal Article Computers & Chemical Engineering, 92 , pp. 18–36, 2016, ISSN: 0098-1354. @article{Durand201618, title = {Economic model predictive control designs for input rate-of-change constraint handling and guaranteed economic performance}, author = {H Durand and M Ellis and P D Christofides}, url = {http://www.sciencedirect.com/science/article/pii/S0098135416301223}, doi = {https://doi.org/10.1016/j.compchemeng.2016.04.026}, issn = {0098-1354}, year = {2016}, date = {2016-01-01}, journal = {Computers & Chemical Engineering}, volume = {92}, pages = {18--36}, abstract = {Economic model predictive control (EMPC) has been a popular topic in the recent chemical process control literature due to its potential to improve process profit by operating a system in a time-varying manner. However, time-varying operation may cause excessive wear of the process components such as valves and pumps. To address this issue, input magnitude constraints and input rate-of-change constraints can be added to the EMPC optimization problem to prevent possible frequent and extreme changes in the requested inputs. Specifically, we develop input rate-of-change constraints that can be incorporated in Lyapunov-based EMPC (LEMPC) that ensure controller feasibility and closed-loop stability. Furthermore, we develop a terminal equality constraint for LEMPC that can ensure that the performance of LEMPC is at least as good as that of a Lyapunov-based controller in finite-time and in infinite-time. Chemical process examples demonstrate the incorporation of input rate-of-change constraints and terminal state constraints in EMPC.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Economic model predictive control (EMPC) has been a popular topic in the recent chemical process control literature due to its potential to improve process profit by operating a system in a time-varying manner. However, time-varying operation may cause excessive wear of the process components such as valves and pumps. To address this issue, input magnitude constraints and input rate-of-change constraints can be added to the EMPC optimization problem to prevent possible frequent and extreme changes in the requested inputs. Specifically, we develop input rate-of-change constraints that can be incorporated in Lyapunov-based EMPC (LEMPC) that ensure controller feasibility and closed-loop stability. Furthermore, we develop a terminal equality constraint for LEMPC that can ensure that the performance of LEMPC is at least as good as that of a Lyapunov-based controller in finite-time and in infinite-time. Chemical process examples demonstrate the incorporation of input rate-of-change constraints and terminal state constraints in EMPC. |
2015 |
Ellis, M; Christofides, P D Real-time economic model predictive control of nonlinear process systems Journal Article AIChE Journal, 61 , pp. 555–571, 2015, ISSN: 1547-5905. @article{Ellis2015555, title = {Real-time economic model predictive control of nonlinear process systems}, author = {M Ellis and P D Christofides}, url = {http://dx.doi.org/10.1002/aic.14673}, doi = {10.1002/aic.14673}, issn = {1547-5905}, year = {2015}, date = {2015-01-01}, journal = {AIChE Journal}, volume = {61}, pages = {555--571}, abstract = {Closed‐loop stability of nonlinear systems under real‐time Lyapunov‐based economic model predictive control (LEMPC) with potentially unknown and time‐varying computational delay is considered. To address guaranteed closed‐loop stability (in the sense of boundedness of the closed‐loop state in a compact state‐space set), an implementation strategy is proposed which features a triggered evaluation of the LEMPC optimization problem to compute an input trajectory over a finite‐time prediction horizon in advance. At each sampling period, stability conditions must be satisfied for the precomputed LEMPC control action to be applied to the closed‐loop system. If the stability conditions are not satisfied, a backup explicit stabilizing controller is applied over the sampling period. Closed‐loop stability under the real‐time LEMPC strategy is analyzed and specific stability conditions are derived. The real‐time LEMPC scheme is applied to a chemical process network example to demonstrate closed‐loop stability and closed‐loop economic performance improvement over that achieved for operation at the economically optimal steady state.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Closed‐loop stability of nonlinear systems under real‐time Lyapunov‐based economic model predictive control (LEMPC) with potentially unknown and time‐varying computational delay is considered. To address guaranteed closed‐loop stability (in the sense of boundedness of the closed‐loop state in a compact state‐space set), an implementation strategy is proposed which features a triggered evaluation of the LEMPC optimization problem to compute an input trajectory over a finite‐time prediction horizon in advance. At each sampling period, stability conditions must be satisfied for the precomputed LEMPC control action to be applied to the closed‐loop system. If the stability conditions are not satisfied, a backup explicit stabilizing controller is applied over the sampling period. Closed‐loop stability under the real‐time LEMPC strategy is analyzed and specific stability conditions are derived. The real‐time LEMPC scheme is applied to a chemical process network example to demonstrate closed‐loop stability and closed‐loop economic performance improvement over that achieved for operation at the economically optimal steady state. |
Alanqar, A; Ellis, M; Christofides, P D Economic model predictive control of nonlinear process systems using empirical models Journal Article AIChE Journal, 61 , pp. 816–830, 2015, ISSN: 1547-5905. @article{Alanqar2015816, title = {Economic model predictive control of nonlinear process systems using empirical models}, author = {A Alanqar and M Ellis and P D Christofides}, url = {http://dx.doi.org/10.1002/aic.14683}, doi = {10.1002/aic.14683}, issn = {1547-5905}, year = {2015}, date = {2015-01-01}, journal = {AIChE Journal}, volume = {61}, pages = {816--830}, abstract = {Economic model predictive control (EMPC) is a feedback control technique that attempts to tightly integrate economic optimization and feedback control since it is a predictive control scheme that is formulated with an objective function representing the process economics. As its name implies, EMPC requires the availability of a dynamic model to compute its control actions and such a model may be obtained either through application of first principles or through system identification techniques. In industrial practice, it may be difficult in general to obtain an accurate first‐principles model of the process. Motivated by this, in the present work, Lyapunov‐based EMPC (LEMPC) is designed with a linear empirical model that allows for closed‐loop stability guarantees in the context of nonlinear chemical processes. Specifically, when the linear model provides a sufficient degree of accuracy in the region where time varying economically optimal operation is considered, conditions for closed‐loop stability under the LEMPC scheme based on the empirical model are derived. The LEMPC scheme is applied to a chemical process example to demonstrate its closed‐loop stability and performance properties as well as significant computational advantages.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Economic model predictive control (EMPC) is a feedback control technique that attempts to tightly integrate economic optimization and feedback control since it is a predictive control scheme that is formulated with an objective function representing the process economics. As its name implies, EMPC requires the availability of a dynamic model to compute its control actions and such a model may be obtained either through application of first principles or through system identification techniques. In industrial practice, it may be difficult in general to obtain an accurate first‐principles model of the process. Motivated by this, in the present work, Lyapunov‐based EMPC (LEMPC) is designed with a linear empirical model that allows for closed‐loop stability guarantees in the context of nonlinear chemical processes. Specifically, when the linear model provides a sufficient degree of accuracy in the region where time varying economically optimal operation is considered, conditions for closed‐loop stability under the LEMPC scheme based on the empirical model are derived. The LEMPC scheme is applied to a chemical process example to demonstrate its closed‐loop stability and performance properties as well as significant computational advantages. |
Lao, L; Ellis, M; Christofides, P D Handling state constraints and economics in feedback control of transport-reaction processes Journal Article Journal of Process Control, 32 , pp. 98–108, 2015, ISSN: 0959-1524. @article{Lao201598, title = {Handling state constraints and economics in feedback control of transport-reaction processes}, author = {L Lao and M Ellis and P D Christofides}, url = {http://www.sciencedirect.com/science/article/pii/S095915241500075X}, doi = {http://dx.doi.org/10.1016/j.jprocont.2015.04.009}, issn = {0959-1524}, year = {2015}, date = {2015-01-01}, journal = {Journal of Process Control}, volume = {32}, pages = {98--108}, abstract = {Transport-reaction processes, which are typically described by parabolic partial differential equations (PDEs), play an important role within the chemical process industries. Therefore, it is important to develop feedback control techniques that operate transport-reaction processes in an economically optimal fashion in the presence of constraints in the process states and manipulated inputs. Economic model predictive control (EMPC) is a predictive control scheme that combines process economics and feedback control into an integrated framework with the potential of improving the closed-loop process economic performance compared to traditional control methodologies. In this work, we focus on systems of nonlinear parabolic PDEs and propose a novel EMPC design integrating adaptive proper orthogonal decomposition (APOD) method with a high-order finite-difference method to handle state constraints. The computational efficiency and constraint handling properties of this design are evaluated using a tubular reactor example modeled by two nonlinear parabolic PDEs.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Transport-reaction processes, which are typically described by parabolic partial differential equations (PDEs), play an important role within the chemical process industries. Therefore, it is important to develop feedback control techniques that operate transport-reaction processes in an economically optimal fashion in the presence of constraints in the process states and manipulated inputs. Economic model predictive control (EMPC) is a predictive control scheme that combines process economics and feedback control into an integrated framework with the potential of improving the closed-loop process economic performance compared to traditional control methodologies. In this work, we focus on systems of nonlinear parabolic PDEs and propose a novel EMPC design integrating adaptive proper orthogonal decomposition (APOD) method with a high-order finite-difference method to handle state constraints. The computational efficiency and constraint handling properties of this design are evaluated using a tubular reactor example modeled by two nonlinear parabolic PDEs. |
Lao, L; Ellis, M; Durand, H; Christofides, P D Real-time preventive sensor maintenance using robust moving horizon estimation and economic model predictive control Journal Article AIChE Journal, 61 , pp. 3374–3389, 2015, ISSN: 1547-5905. @article{Lao20153374, title = {Real-time preventive sensor maintenance using robust moving horizon estimation and economic model predictive control}, author = {L Lao and M Ellis and H Durand and P D Christofides}, url = {http://dx.doi.org/10.1002/aic.14960}, doi = {10.1002/aic.14960}, issn = {1547-5905}, year = {2015}, date = {2015-01-01}, journal = {AIChE Journal}, volume = {61}, pages = {3374--3389}, abstract = {Conducting preventive maintenance of measurement sensors in real‐time during process operation under feedback control while ensuring the reliability and improving the economic performance of a process is a central problem of the research area focusing on closed‐loop preventive maintenance of sensors and actuators. To address this problem, a robust moving horizon estimation (RMHE) scheme and an economic model predictive control system are combined to simultaneously achieve preventive sensor maintenance and optimal process economic performance with closed‐loop stability. Specifically, given a preventive sensor maintenance schedule, a RMHE scheme is developed that accommodates varying numbers of sensors to continuously supply accurate state estimates to a Lyapunov‐based economic model predictive control (LEMPC) system. Closed‐loop stability for this control approach can be proven under fairly general observability and stabilizability assumptions to be made precise in the manuscript. Subsequently, a chemical process example incorporating this RMHE‐based LEMPC scheme demonstrates its ability to maintain process stability and achieve optimal process economic performance as scheduled preventive maintenance is performed on the sensors.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Conducting preventive maintenance of measurement sensors in real‐time during process operation under feedback control while ensuring the reliability and improving the economic performance of a process is a central problem of the research area focusing on closed‐loop preventive maintenance of sensors and actuators. To address this problem, a robust moving horizon estimation (RMHE) scheme and an economic model predictive control system are combined to simultaneously achieve preventive sensor maintenance and optimal process economic performance with closed‐loop stability. Specifically, given a preventive sensor maintenance schedule, a RMHE scheme is developed that accommodates varying numbers of sensors to continuously supply accurate state estimates to a Lyapunov‐based economic model predictive control (LEMPC) system. Closed‐loop stability for this control approach can be proven under fairly general observability and stabilizability assumptions to be made precise in the manuscript. Subsequently, a chemical process example incorporating this RMHE‐based LEMPC scheme demonstrates its ability to maintain process stability and achieve optimal process economic performance as scheduled preventive maintenance is performed on the sensors. |
Ellis, M; Christofides, P D Economic model predictive control of nonlinear time-delay systems: Closed-loop stability and delay compensation Journal Article AIChE Journal, 61 , pp. 4152–4165, 2015, ISSN: 1547-5905. @article{Ellis20154152, title = {Economic model predictive control of nonlinear time-delay systems: Closed-loop stability and delay compensation}, author = {M Ellis and P D Christofides}, url = {http://dx.doi.org/10.1002/aic.14964}, doi = {10.1002/aic.14964}, issn = {1547-5905}, year = {2015}, date = {2015-01-01}, journal = {AIChE Journal}, volume = {61}, pages = {4152--4165}, abstract = {Closed‐loop stability of nonlinear time‐delay systems under Lyapunov‐based economic model predictive control (LEMPC) is considered. LEMPC is initially formulated with an ordinary differential equation model and is designed on the basis of an explicit stabilizing control law. To address closed‐loop stability under LEMPC, first, we consider the stability properties of the sampled‐data system resulting from the nonlinear continuous‐time delay system with state and input delay under a sample‐and‐hold implementation of the explicit controller. The steady‐state of this sampled‐data closed‐loop system is shown to be practically stable. Second, conditions such that closed‐loop stability, in the sense of boundedness of the closed‐loop state, under LEMPC are derived. A chemical process example is used to demonstrate that indeed closed‐loop stability is maintained under LEMPC for sufficiently small time‐delays. To cope with performance degradation owing to the effect of input delay, a predictor feedback LEMPC methodology is also proposed. The predictor feedback LEMPC design employs a predictor to compute a prediction of the state after the input delay period and an LEMPC scheme that is formulated with a differential difference equation (DDE) model, which describes the time‐delay system, initialized with the predicted state. The predictor feedback LEMPC is also applied to the chemical process example and yields improved closed‐loop stability and economic performance properties.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Closed‐loop stability of nonlinear time‐delay systems under Lyapunov‐based economic model predictive control (LEMPC) is considered. LEMPC is initially formulated with an ordinary differential equation model and is designed on the basis of an explicit stabilizing control law. To address closed‐loop stability under LEMPC, first, we consider the stability properties of the sampled‐data system resulting from the nonlinear continuous‐time delay system with state and input delay under a sample‐and‐hold implementation of the explicit controller. The steady‐state of this sampled‐data closed‐loop system is shown to be practically stable. Second, conditions such that closed‐loop stability, in the sense of boundedness of the closed‐loop state, under LEMPC are derived. A chemical process example is used to demonstrate that indeed closed‐loop stability is maintained under LEMPC for sufficiently small time‐delays. To cope with performance degradation owing to the effect of input delay, a predictor feedback LEMPC methodology is also proposed. The predictor feedback LEMPC design employs a predictor to compute a prediction of the state after the input delay period and an LEMPC scheme that is formulated with a differential difference equation (DDE) model, which describes the time‐delay system, initialized with the predicted state. The predictor feedback LEMPC is also applied to the chemical process example and yields improved closed‐loop stability and economic performance properties. |
2014 |
Ellis, M; Christofides, P D Integrating dynamic economic optimization and model predictive control for optimal operation of nonlinear process systems Journal Article Control Engineering Practice, 22 , pp. 242–251, 2014, ISSN: 0967-0661. @article{Ellis2014242, title = {Integrating dynamic economic optimization and model predictive control for optimal operation of nonlinear process systems}, author = {M Ellis and P D Christofides}, url = {http://www.sciencedirect.com/science/article/pii/S0967066113000348}, doi = {http://dx.doi.org/10.1016/j.conengprac.2013.02.016}, issn = {0967-0661}, year = {2014}, date = {2014-01-01}, journal = {Control Engineering Practice}, volume = {22}, pages = {242--251}, abstract = {In this work, we propose a conceptual framework for integrating dynamic economic optimization and model predictive control (MPC) for optimal operation of nonlinear process systems. First, we introduce the proposed two-layer integrated framework. The upper layer, consisting of an economic MPC (EMPC) system that receives state feedback and time-dependent economic information, computes economically optimal time-varying operating trajectories for the process by optimizing a time-dependent economic cost function over a finite prediction horizon subject to a nonlinear dynamic process model. The lower feedback control layer may utilize conventional MPC schemes or even classical control to compute feedback control actions that force the process state to track the time-varying operating trajectories computed by the upper layer EMPC. Such a framework takes advantage of the EMPC ability to compute optimal process time-varying operating policies using a dynamic process model instead of a steady-state model, and the incorporation of suitable constraints on the EMPC allows calculating operating process state trajectories that can be tracked by the control layer. Second, we prove practical closed-loop stability including an explicit characterization of the closed-loop stability region. Finally, we demonstrate through extensive simulations using a chemical process model that the proposed framework can both (1) achieve stability and (2) lead to improved economic closed-loop performance compared to real-time optimization (RTO) systems using steady-state models.}, keywords = {}, pubstate = {published}, tppubtype = {article} } In this work, we propose a conceptual framework for integrating dynamic economic optimization and model predictive control (MPC) for optimal operation of nonlinear process systems. First, we introduce the proposed two-layer integrated framework. The upper layer, consisting of an economic MPC (EMPC) system that receives state feedback and time-dependent economic information, computes economically optimal time-varying operating trajectories for the process by optimizing a time-dependent economic cost function over a finite prediction horizon subject to a nonlinear dynamic process model. The lower feedback control layer may utilize conventional MPC schemes or even classical control to compute feedback control actions that force the process state to track the time-varying operating trajectories computed by the upper layer EMPC. Such a framework takes advantage of the EMPC ability to compute optimal process time-varying operating policies using a dynamic process model instead of a steady-state model, and the incorporation of suitable constraints on the EMPC allows calculating operating process state trajectories that can be tracked by the control layer. Second, we prove practical closed-loop stability including an explicit characterization of the closed-loop stability region. Finally, we demonstrate through extensive simulations using a chemical process model that the proposed framework can both (1) achieve stability and (2) lead to improved economic closed-loop performance compared to real-time optimization (RTO) systems using steady-state models. |
Ellis, M; Christofides, P D Economic model predictive control with time-varying objective function for nonlinear process systems Journal Article AIChE Journal, 60 , pp. 507–519, 2014, ISSN: 1547-5905. @article{Ellis2014507, title = {Economic model predictive control with time-varying objective function for nonlinear process systems}, author = {M Ellis and P D Christofides}, url = {http://dx.doi.org/10.1002/aic.14274}, doi = {10.1002/aic.14274}, issn = {1547-5905}, year = {2014}, date = {2014-01-01}, journal = {AIChE Journal}, volume = {60}, pages = {507--519}, abstract = {Economic model predictive control (EMPC) is a control scheme that combines real‐time dynamic economic process optimization with the feedback properties of model predictive control (MPC) by replacing the quadratic cost function with a general economic cost function. Almost all the recent work on EMPC involves cost functions that are time invariant (do not explicitly account for time‐varying process economics). In the present work, we focus on the development of a Lyapunov‐based EMPC (LEMPC) scheme that is formulated with an explicitly time‐varying economic cost function. First, the formulation of the proposed two‐mode LEMPC is given. Second, closed‐loop stability is proven through a theoretical treatment. Last, we demonstrate through extensive closed‐loop simulations of a chemical process that the proposed LEMPC can achieve stability with time‐varying economic cost as well as improve economic performance of the process over a conventional MPC scheme.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Economic model predictive control (EMPC) is a control scheme that combines real‐time dynamic economic process optimization with the feedback properties of model predictive control (MPC) by replacing the quadratic cost function with a general economic cost function. Almost all the recent work on EMPC involves cost functions that are time invariant (do not explicitly account for time‐varying process economics). In the present work, we focus on the development of a Lyapunov‐based EMPC (LEMPC) scheme that is formulated with an explicitly time‐varying economic cost function. First, the formulation of the proposed two‐mode LEMPC is given. Second, closed‐loop stability is proven through a theoretical treatment. Last, we demonstrate through extensive closed‐loop simulations of a chemical process that the proposed LEMPC can achieve stability with time‐varying economic cost as well as improve economic performance of the process over a conventional MPC scheme. |
Ellis, M; Christofides, P D Optimal time-varying operation of nonlinear process systems with economic model predictive control Journal Article Industrial & Engineering Chemistry Research, 53 , pp. 4991–5001, 2014. @article{Ellis20144991, title = {Optimal time-varying operation of nonlinear process systems with economic model predictive control}, author = {M Ellis and P D Christofides}, url = {http://pubs.acs.org/doi/abs/10.1021/ie303537e}, doi = {10.1021/ie303537e}, year = {2014}, date = {2014-01-01}, journal = {Industrial & Engineering Chemistry Research}, volume = {53}, pages = {4991--5001}, abstract = {In this work, we propose a two-layer approach to dynamic economic optimization and process control for optimal time-varying operation of nonlinear process systems. The upper layer, utilizing a Lyapunov-based economic model predictive control (LEMPC) system, is used to compute dynamic economic optimization policies for process operation. The lower layer, utilizing a Lyapunov-based MPC (LMPC) system, is used to ensure that the closed-loop system state follows the optimal time-varying trajectories computed by the upper layer over each finite-time operating window. To improve the computational efficiency of the two-layer structure, we allow both the LEMPC and the LMPC to compute control actions for two distinct sets of manipulated inputs thus decreasing the real-time computational demand compared to other one-layer EMPC schemes. Following a rigorous formulation and analysis of the proposed method, we demonstrate boundedness of the closed-loop system state and closed-loop economic performance improvement with the proposed two-layer framework compared to steady-state operation as well as with respect to other existing time-varying operating strategies previously proposed in the literature in the context of a benchmark chemical process application.}, keywords = {}, pubstate = {published}, tppubtype = {article} } In this work, we propose a two-layer approach to dynamic economic optimization and process control for optimal time-varying operation of nonlinear process systems. The upper layer, utilizing a Lyapunov-based economic model predictive control (LEMPC) system, is used to compute dynamic economic optimization policies for process operation. The lower layer, utilizing a Lyapunov-based MPC (LMPC) system, is used to ensure that the closed-loop system state follows the optimal time-varying trajectories computed by the upper layer over each finite-time operating window. To improve the computational efficiency of the two-layer structure, we allow both the LEMPC and the LMPC to compute control actions for two distinct sets of manipulated inputs thus decreasing the real-time computational demand compared to other one-layer EMPC schemes. Following a rigorous formulation and analysis of the proposed method, we demonstrate boundedness of the closed-loop system state and closed-loop economic performance improvement with the proposed two-layer framework compared to steady-state operation as well as with respect to other existing time-varying operating strategies previously proposed in the literature in the context of a benchmark chemical process application. |
Lao, L; Ellis, M; Christofides, P D Economic model predictive control of parabolic PDE systems: Addressing state estimation and computational efficiency Journal Article Journal of Process Control, 24 , pp. 448–462, 2014, ISSN: 0959-1524. @article{Lao2014448, title = {Economic model predictive control of parabolic PDE systems: Addressing state estimation and computational efficiency}, author = {L Lao and M Ellis and P D Christofides}, url = {http://www.sciencedirect.com/science/article/pii/S0959152414000316}, doi = {http://dx.doi.org/10.1016/j.jprocont.2014.01.007}, issn = {0959-1524}, year = {2014}, date = {2014-01-01}, journal = {Journal of Process Control}, volume = {24}, pages = {448--462}, abstract = {In a previous work, an economic model predictive control (EMPC) system for parabolic partial differential equation (PDE) systems was proposed. Through operating the PDE system in a time-varying fashion, the EMPC system demonstrated improved economic performance over steady-state operation. The EMPC system assumed the knowledge of the complete state spatial profile at each sampling period. From a practical point of view, measurements of the state variables are typically only available at a finite number of spatial positions. Additionally, the basis functions used to construct a reduced-order model (ROM) for the EMPC system were derived using analytical sinusoidal/cosinusoidal eigenfunctions. However, constructing a ROM on the basis of historical data-based empirical eigenfunctions by applying Karhunen-Loève expansion may be more computationally efficient. To address these issues, several EMPC systems are formulated for both output feedback implementation and with ROMs based on analytical sinusoidal/cosinusoidal eigenfunctions and empirical eigenfunctions. The EMPC systems are evaluated using a non-isothermal tubular reactor example, described by two nonlinear parabolic PDEs, where a second-order reaction takes place. The model accuracy, computational time, input and state constraint satisfaction, and closed-loop economic performance of the closed-loop tubular reactor under the different EMPC systems are compared.}, keywords = {}, pubstate = {published}, tppubtype = {article} } In a previous work, an economic model predictive control (EMPC) system for parabolic partial differential equation (PDE) systems was proposed. Through operating the PDE system in a time-varying fashion, the EMPC system demonstrated improved economic performance over steady-state operation. The EMPC system assumed the knowledge of the complete state spatial profile at each sampling period. From a practical point of view, measurements of the state variables are typically only available at a finite number of spatial positions. Additionally, the basis functions used to construct a reduced-order model (ROM) for the EMPC system were derived using analytical sinusoidal/cosinusoidal eigenfunctions. However, constructing a ROM on the basis of historical data-based empirical eigenfunctions by applying Karhunen-Loève expansion may be more computationally efficient. To address these issues, several EMPC systems are formulated for both output feedback implementation and with ROMs based on analytical sinusoidal/cosinusoidal eigenfunctions and empirical eigenfunctions. The EMPC systems are evaluated using a non-isothermal tubular reactor example, described by two nonlinear parabolic PDEs, where a second-order reaction takes place. The model accuracy, computational time, input and state constraint satisfaction, and closed-loop economic performance of the closed-loop tubular reactor under the different EMPC systems are compared. |
Ellis, M; Zhang, J; Liu, J; Christofides, P D Robust moving horizon estimation based output feedback economic model predictive control Journal Article Systems & Control Letters, 68 , pp. 101–109, 2014, ISSN: 0167-6911. @article{Ellis2014101, title = {Robust moving horizon estimation based output feedback economic model predictive control}, author = {M Ellis and J Zhang and J Liu and P D Christofides}, url = {http://www.sciencedirect.com/science/article/pii/S0167691114000590}, doi = {http://dx.doi.org/10.1016/j.sysconle.2014.03.003}, issn = {0167-6911}, year = {2014}, date = {2014-01-01}, journal = {Systems & Control Letters}, volume = {68}, pages = {101--109}, abstract = {In this work, we develop an economic model predictive control scheme for a class of nonlinear systems with bounded process and measurement noise. In order to achieve fast convergence of the state estimates to the actual system state as well as the robustness of the observer to measurement and process noise, a deterministic (high-gain) observer is first applied for a small time period with continuous output measurements to drive the estimation error to a small value; after this initial small time period, a robust moving horizon estimation scheme is used on-line to provide more accurate and smoother state estimates. In the design of the robust moving horizon estimation scheme, the deterministic observer is used to calculate reference estimates and confidence regions that contain the actual system state. Within the confidence regions, the moving horizon estimation scheme is allowed to optimize its estimates. The output feedback economic model predictive controller is designed via Lyapunov techniques based on state estimates provided by the deterministic observer and the moving horizon estimation scheme. The stability of the closed-loop system is analyzed rigorously and conditions that ensure the closed-loop stability are derived. Extensive simulations based on a chemical process example illustrate the effectiveness of the proposed approach.}, keywords = {}, pubstate = {published}, tppubtype = {article} } In this work, we develop an economic model predictive control scheme for a class of nonlinear systems with bounded process and measurement noise. In order to achieve fast convergence of the state estimates to the actual system state as well as the robustness of the observer to measurement and process noise, a deterministic (high-gain) observer is first applied for a small time period with continuous output measurements to drive the estimation error to a small value; after this initial small time period, a robust moving horizon estimation scheme is used on-line to provide more accurate and smoother state estimates. In the design of the robust moving horizon estimation scheme, the deterministic observer is used to calculate reference estimates and confidence regions that contain the actual system state. Within the confidence regions, the moving horizon estimation scheme is allowed to optimize its estimates. The output feedback economic model predictive controller is designed via Lyapunov techniques based on state estimates provided by the deterministic observer and the moving horizon estimation scheme. The stability of the closed-loop system is analyzed rigorously and conditions that ensure the closed-loop stability are derived. Extensive simulations based on a chemical process example illustrate the effectiveness of the proposed approach. |
Lao, L; Ellis, M; Christofides, P D Smart manufacturing: Ħandling preventive actuator maintenance and economics using model predictive control Journal Article AIChE Journal, 60 , pp. 2179–2196, 2014, ISSN: 1547-5905. @article{Lao20142179, title = {Smart manufacturing: Ħandling preventive actuator maintenance and economics using model predictive control}, author = {L Lao and M Ellis and P D Christofides}, url = {http://dx.doi.org/10.1002/aic.14427}, doi = {10.1002/aic.14427}, issn = {1547-5905}, year = {2014}, date = {2014-01-01}, journal = {AIChE Journal}, volume = {60}, pages = {2179--2196}, abstract = {Integrating components and systems of the manufacturing process is an important area of research to enable the future development and deployment of the Smart Manufacturing paradigm. An economic model predictive control (EMPC) scheme is proposed that effectively integrates scheduled preventive control actuator maintenance, process economics, and process control into a unified methodology. To accomplish this goal, a Lyapunov‐based EMPC (LEMPC) scheme is formulated for handling changing number of online actuators (i.e., changing number of manipulated inputs). Closed‐loop stability under the proposed LEMPC is proven. Subsequently, the LEMPC is applied to a chemical process network used for benzene alkylation to demonstrate that the LEMPC can maintain stability and improve dynamic economic performance of the process network in the presence of changing number of available control actuators resulting from scheduled preventive maintenance tasks.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Integrating components and systems of the manufacturing process is an important area of research to enable the future development and deployment of the Smart Manufacturing paradigm. An economic model predictive control (EMPC) scheme is proposed that effectively integrates scheduled preventive control actuator maintenance, process economics, and process control into a unified methodology. To accomplish this goal, a Lyapunov‐based EMPC (LEMPC) scheme is formulated for handling changing number of online actuators (i.e., changing number of manipulated inputs). Closed‐loop stability under the proposed LEMPC is proven. Subsequently, the LEMPC is applied to a chemical process network used for benzene alkylation to demonstrate that the LEMPC can maintain stability and improve dynamic economic performance of the process network in the presence of changing number of available control actuators resulting from scheduled preventive maintenance tasks. |
Lao, L; Ellis, M; Christofides, P D Economic model predictive control of transport-reaction processes Journal Article Industrial & Engineering Chemistry Research, 53 , pp. 7382–7396, 2014. @article{Lao20147383, title = {Economic model predictive control of transport-reaction processes}, author = {L Lao and M Ellis and P D Christofides}, url = {http://dx.doi.org/10.1021/ie401016a}, doi = {10.1021/ie401016a}, year = {2014}, date = {2014-01-01}, journal = {Industrial & Engineering Chemistry Research}, volume = {53}, pages = {7382--7396}, abstract = {This work focuses on the development of economic model predictive control (EMPC) systems for transport-reaction processes described by nonlinear parabolic partial differential equations (PDEs) and their applications to a non-isothermal tubular reactor where a second-order chemical reaction takes place. The tubular reactor is modeled by two nonlinear parabolic PDEs. Galerkin’s method is used to derive finite-dimensional systems that capture the dominant dynamics of the parabolic PDEs which are subsequently used for the EMPC design. The EMPC formulation uses the integral of the reaction rate along the length of the reactor as an economic cost function subject to constraints on the control action and states over an operation period. Closed-loop simulations are conducted of a low-order EMPC system, formulated with a constraint on the available reactant material over each operation period, applied to a high-order discretization of the PDEs and of a high-order EMPC system formulated with a specific state constraint and with the constraint on the available reactant material. Simulation results demonstrate that the EMPC operates the process in a time-varying fashion and improves the economic cost over steady-state operation using the same amount of reactant material over a fixed period of operation, as well as meeting state constraints.}, keywords = {}, pubstate = {published}, tppubtype = {article} } This work focuses on the development of economic model predictive control (EMPC) systems for transport-reaction processes described by nonlinear parabolic partial differential equations (PDEs) and their applications to a non-isothermal tubular reactor where a second-order chemical reaction takes place. The tubular reactor is modeled by two nonlinear parabolic PDEs. Galerkin’s method is used to derive finite-dimensional systems that capture the dominant dynamics of the parabolic PDEs which are subsequently used for the EMPC design. The EMPC formulation uses the integral of the reaction rate along the length of the reactor as an economic cost function subject to constraints on the control action and states over an operation period. Closed-loop simulations are conducted of a low-order EMPC system, formulated with a constraint on the available reactant material over each operation period, applied to a high-order discretization of the PDEs and of a high-order EMPC system formulated with a specific state constraint and with the constraint on the available reactant material. Simulation results demonstrate that the EMPC operates the process in a time-varying fashion and improves the economic cost over steady-state operation using the same amount of reactant material over a fixed period of operation, as well as meeting state constraints. |
Ellis, M; Durand, H; Christofides, P D A tutorial review of economic model predictive control methods Journal Article Journal of Process Control, 24 , pp. 1156–1178, 2014, ISSN: 0959-1524. @article{Ellis20141156, title = {A tutorial review of economic model predictive control methods}, author = {M Ellis and H Durand and P D Christofides}, url = {http://www.sciencedirect.com/science/article/pii/S0959152414000900}, doi = {http://dx.doi.org/10.1016/j.jprocont.2014.03.010}, issn = {0959-1524}, year = {2014}, date = {2014-01-01}, journal = {Journal of Process Control}, volume = {24}, pages = {1156--1178}, abstract = {An overview of the recent results on economic model predictive control (EMPC) is presented and discussed addressing both closed-loop stability and performance for nonlinear systems. A chemical process example is used to provide a demonstration of a few of the various approaches. The paper concludes with a brief discussion of the current status of EMPC and future research directions to promote and stimulate further research potential in this area.}, keywords = {}, pubstate = {published}, tppubtype = {article} } An overview of the recent results on economic model predictive control (EMPC) is presented and discussed addressing both closed-loop stability and performance for nonlinear systems. A chemical process example is used to provide a demonstration of a few of the various approaches. The paper concludes with a brief discussion of the current status of EMPC and future research directions to promote and stimulate further research potential in this area. |
Ellis, M; Christofides, P D Selection of control configurations for economic model predictive control systems Journal Article AIChE Journal, 60 , pp. 3230–3242, 2014, ISSN: 1547-5905. @article{Ellis20143230, title = {Selection of control configurations for economic model predictive control systems}, author = {M Ellis and P D Christofides}, url = {http://dx.doi.org/10.1002/aic.14514}, doi = {10.1002/aic.14514}, issn = {1547-5905}, year = {2014}, date = {2014-01-01}, journal = {AIChE Journal}, volume = {60}, pages = {3230--3242}, abstract = {Economic model predictive control (EMPC) is a feedback control method that dictates a potentially dynamic (time‐varying) operating policy to optimize the process economics. The objective function used in the EMPC system may be a general nonlinear function that describes the process/system economics. As this function is not derived on the sole basis of classical control considerations (stabilization, tracking, and optimal control action calculation) but rather on the basis of economics, selecting the appropriate control configuration, and quantifying the influence of a given input on an economic cost is an important task for the proper design and computational efficiency of an EMPC scheme. Owing to these considerations, an input selection methodology for EMPC is proposed which utilizes the relative degree and the sensitivity of the economic cost with respect to an input to identify and select stabilizing manipulated inputs with the most dynamic and steady‐state influence on the economic cost function to be assigned to EMPC. Other considerations for input selection for EMPC are also discussed and integrated into a proposed input selection methodology for EMPC. The control configuration selection method for EMPC is demonstrated using a chemical process example.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Economic model predictive control (EMPC) is a feedback control method that dictates a potentially dynamic (time‐varying) operating policy to optimize the process economics. The objective function used in the EMPC system may be a general nonlinear function that describes the process/system economics. As this function is not derived on the sole basis of classical control considerations (stabilization, tracking, and optimal control action calculation) but rather on the basis of economics, selecting the appropriate control configuration, and quantifying the influence of a given input on an economic cost is an important task for the proper design and computational efficiency of an EMPC scheme. Owing to these considerations, an input selection methodology for EMPC is proposed which utilizes the relative degree and the sensitivity of the economic cost with respect to an input to identify and select stabilizing manipulated inputs with the most dynamic and steady‐state influence on the economic cost function to be assigned to EMPC. Other considerations for input selection for EMPC are also discussed and integrated into a proposed input selection methodology for EMPC. The control configuration selection method for EMPC is demonstrated using a chemical process example. |
Ellis, M; Christofides, P D Performance monitoring of economic model predictive control systems Journal Article Industrial & Engineering Chemistry Research, 53 , pp. 15406–15413, 2014. @article{Ellis201415406, title = {Performance monitoring of economic model predictive control systems}, author = {M Ellis and P D Christofides}, url = {http://dx.doi.org/10.1021/ie403462y}, doi = {10.1021/ie403462y}, year = {2014}, date = {2014-01-01}, journal = {Industrial & Engineering Chemistry Research}, volume = {53}, pages = {15406--15413}, abstract = {A framework for performance monitoring of economic model predictive control (EMPC) systems is presented which includes the computation of an acceptable operating region, which is a well-defined region in state-space, for EMPC systems to operate a process in a time-varying fashion to optimize process economics while meeting input constraints and stabilizability requirements. To capture the interplay between sources of common cause variance caused by various sources like sensor noise, imperfect actuator operation, and model inaccuracy, a residual variable taken to be the difference of actual real-time economic cost and the predicted (expected) economic cost is defined. Utilizing exponentially weighted moving average (EWMA) and historical closed-loop process data, an upper control limit and a lower control limit are established which defines normal operation (i.e., operation with common cause variation). The limits are utilized to monitor the performance of EMPC by comparing real-time process operation data under EMPC and the corresponding regions of acceptable EMPC operation computed in the normal operation dynamic data generation step. The proposed monitoring framework is demonstrated and evaluated using a chemical process example.}, keywords = {}, pubstate = {published}, tppubtype = {article} } A framework for performance monitoring of economic model predictive control (EMPC) systems is presented which includes the computation of an acceptable operating region, which is a well-defined region in state-space, for EMPC systems to operate a process in a time-varying fashion to optimize process economics while meeting input constraints and stabilizability requirements. To capture the interplay between sources of common cause variance caused by various sources like sensor noise, imperfect actuator operation, and model inaccuracy, a residual variable taken to be the difference of actual real-time economic cost and the predicted (expected) economic cost is defined. Utilizing exponentially weighted moving average (EWMA) and historical closed-loop process data, an upper control limit and a lower control limit are established which defines normal operation (i.e., operation with common cause variation). The limits are utilized to monitor the performance of EMPC by comparing real-time process operation data under EMPC and the corresponding regions of acceptable EMPC operation computed in the normal operation dynamic data generation step. The proposed monitoring framework is demonstrated and evaluated using a chemical process example. |
Ellis, M; Christofides, P D On finite-time and infinite-time cost improvement of economic model predictive control for nonlinear systems Journal Article Automatica, 50 , pp. 2561–2569, 2014, ISSN: 0005-1098. @article{Ellis20142561, title = {On finite-time and infinite-time cost improvement of economic model predictive control for nonlinear systems}, author = {M Ellis and P D Christofides}, url = {http://www.sciencedirect.com/science/article/pii/S0005109814003124}, doi = {http://dx.doi.org/10.1016/j.automatica.2014.08.011}, issn = {0005-1098}, year = {2014}, date = {2014-01-01}, journal = {Automatica}, volume = {50}, pages = {2561--2569}, abstract = {A novel two-layer economic model predictive control (EMPC) structure that addresses provable finite-time and infinite-time closed-loop economic performance of nonlinear systems in closed-loop with EMPC is presented. In the upper layer, a Lyapunov-based EMPC (LEMPC) scheme is formulated with performance constraints by taking advantage of an auxiliary Lyapunov-based model predictive control (LMPC) problem solution formulated with a quadratic cost function. The lower layer LEMPC uses a shorter prediction horizon and smaller sampling period than the upper layer LEMPC and involves explicit performance-based constraints computed by the upper layer LEMPC. Thus, the two-layer architecture allows for dividing dynamic optimization and control tasks into two layers for a computationally manageable control scheme at the feedback control (lower) layer. A chemical process example is used to demonstrate the performance and stability properties of the two-layer LEMPC structure.}, keywords = {}, pubstate = {published}, tppubtype = {article} } A novel two-layer economic model predictive control (EMPC) structure that addresses provable finite-time and infinite-time closed-loop economic performance of nonlinear systems in closed-loop with EMPC is presented. In the upper layer, a Lyapunov-based EMPC (LEMPC) scheme is formulated with performance constraints by taking advantage of an auxiliary Lyapunov-based model predictive control (LMPC) problem solution formulated with a quadratic cost function. The lower layer LEMPC uses a shorter prediction horizon and smaller sampling period than the upper layer LEMPC and involves explicit performance-based constraints computed by the upper layer LEMPC. Thus, the two-layer architecture allows for dividing dynamic optimization and control tasks into two layers for a computationally manageable control scheme at the feedback control (lower) layer. A chemical process example is used to demonstrate the performance and stability properties of the two-layer LEMPC structure. |
Durand, H; Ellis, M; Christofides, P D Integrated design of control actuator layer and economic model predictive control for nonlinear processes Journal Article Industrial & Engineering Chemistry Research, 53 , pp. 20000–20012, 2014. @article{Durand201420000, title = {Integrated design of control actuator layer and economic model predictive control for nonlinear processes}, author = {H Durand and M Ellis and P D Christofides}, url = {http://dx.doi.org/10.1021/ie503934y}, doi = {10.1021/ie503934y}, year = {2014}, date = {2014-01-01}, journal = {Industrial & Engineering Chemistry Research}, volume = {53}, pages = {20000--20012}, abstract = {In the present work, an economic model predictive control (EMPC) system is designed that accounts for the dynamics of the control actuators. A combined process–actuator dynamic model is developed to describe the process and control actuator dynamics and is used within the EMPC system. Integrating the design of the regulatory control layer, which controls the control actuators, and the supervisory control layer consisting of an EMPC system is an important consideration given the fact that EMPC may force an unsteady-state operating policy to optimize the process economics, and the dynamics of the control actuator layer may affect the closed-loop process-actuator dynamics. Moreover, integral or average input constraints are often imposed within the EMPC solution. However, if the actuator layer is not accounted for in the EMPC system, the actuator output trajectory may not satisfy the integral input constraints. To address closed-loop stability of the combined process-actuator closed-loop system, stability constraints, designed via Lyapunov-based techniques, are imposed on the EMPC problem to guarantee closed-loop stability of the process system under the EMPC. An EMPC system accounting for the control actuator dynamics is applied to a benchmark chemical process example to study the impact of the actuator dynamics on closed-loop economic performance and reactant material constraint satisfaction.}, keywords = {}, pubstate = {published}, tppubtype = {article} } In the present work, an economic model predictive control (EMPC) system is designed that accounts for the dynamics of the control actuators. A combined process–actuator dynamic model is developed to describe the process and control actuator dynamics and is used within the EMPC system. Integrating the design of the regulatory control layer, which controls the control actuators, and the supervisory control layer consisting of an EMPC system is an important consideration given the fact that EMPC may force an unsteady-state operating policy to optimize the process economics, and the dynamics of the control actuator layer may affect the closed-loop process-actuator dynamics. Moreover, integral or average input constraints are often imposed within the EMPC solution. However, if the actuator layer is not accounted for in the EMPC system, the actuator output trajectory may not satisfy the integral input constraints. To address closed-loop stability of the combined process-actuator closed-loop system, stability constraints, designed via Lyapunov-based techniques, are imposed on the EMPC problem to guarantee closed-loop stability of the process system under the EMPC. An EMPC system accounting for the control actuator dynamics is applied to a benchmark chemical process example to study the impact of the actuator dynamics on closed-loop economic performance and reactant material constraint satisfaction. |
2013 |
Ellis, M; Heidarinejad, M; Christofides, P D Economic model predictive control of nonlinear singularly perturbed systems Journal Article Journal of Process Control, 23 , pp. 743–754, 2013, ISSN: 0959-1524. @article{Ellis2013743, title = {Economic model predictive control of nonlinear singularly perturbed systems}, author = {M Ellis and M Heidarinejad and P D Christofides}, url = {http://www.sciencedirect.com/science/article/pii/S0959152413000346}, doi = {10.1016/j.jprocont.2013.03.001}, issn = {0959-1524}, year = {2013}, date = {2013-01-01}, journal = {Journal of Process Control}, volume = {23}, pages = {743--754}, abstract = {We focus on the development of a Lyapunov-based economic model predictive control (LEMPC) method for nonlinear singularly perturbed systems in standard form arising naturally in the modeling of two-time-scale chemical processes. A composite control structure is proposed in which, a "fast” Lyapunov-based model predictive controller (LMPC) using a quadratic cost function which penalizes the deviation of the fast states from their equilibrium slow manifold and the corresponding manipulated inputs, is used to stabilize the fast dynamics while a two-mode “slow” LEMPC design is used on the slow subsystem that addresses economic considerations as well as desired closed-loop stability properties by utilizing an economic (typically non-quadratic) cost function in its formulation and possibly dictating a time-varying process operation. Through a multirate measurement sampling scheme, fast sampling of the fast state variables is used in the fast LMPC while slow-sampling of the slow state variables is used in the slow LEMPC. Appropriate stabilizability assumptions are made and suitable constraints are imposed on the proposed control scheme to guarantee the closed-loop stability and singular perturbation theory is used to analyze the closed-loop system. The proposed control method is demonstrated through a nonlinear chemical process example.}, keywords = {}, pubstate = {published}, tppubtype = {article} } We focus on the development of a Lyapunov-based economic model predictive control (LEMPC) method for nonlinear singularly perturbed systems in standard form arising naturally in the modeling of two-time-scale chemical processes. A composite control structure is proposed in which, a "fast” Lyapunov-based model predictive controller (LMPC) using a quadratic cost function which penalizes the deviation of the fast states from their equilibrium slow manifold and the corresponding manipulated inputs, is used to stabilize the fast dynamics while a two-mode “slow” LEMPC design is used on the slow subsystem that addresses economic considerations as well as desired closed-loop stability properties by utilizing an economic (typically non-quadratic) cost function in its formulation and possibly dictating a time-varying process operation. Through a multirate measurement sampling scheme, fast sampling of the fast state variables is used in the fast LMPC while slow-sampling of the slow state variables is used in the slow LEMPC. Appropriate stabilizability assumptions are made and suitable constraints are imposed on the proposed control scheme to guarantee the closed-loop stability and singular perturbation theory is used to analyze the closed-loop system. The proposed control method is demonstrated through a nonlinear chemical process example. |
Lao, L; Ellis, M; Christofides, P D Proactive fault-tolerant model predictive control Journal Article AIChE Journal, 59 , pp. 2810–2820, 2013, ISSN: 1547-5905. @article{Lao20132810, title = {Proactive fault-tolerant model predictive control}, author = {L Lao and M Ellis and P D Christofides}, url = {http://dx.doi.org/10.1002/aic.14074}, doi = {10.1002/aic.14074}, issn = {1547-5905}, year = {2013}, date = {2013-01-01}, journal = {AIChE Journal}, volume = {59}, pages = {2810--2820}, abstract = {Fault‐tolerant control methods have been extensively researched over the last 10 years in the context of chemical process control applications, and provide a natural framework for integrating process monitoring and control aspects in a way that not only fault detection and isolation but also control system reconfiguration is achieved in the event of a process or actuator fault. But almost all the efforts are focused on the reactive fault‐tolerant control. As another way for fault‐tolerant control, proactive fault‐tolerant control has been a popular topic in the communication systems and aerospace control systems communities for the last 10 years. At this point, no work has been done on proactive fault‐tolerant control within the context of chemical process control. Motivated by this, a proactive fault‐tolerant Lyapunov‐based model predictive controller (LMPC) that can effectively deal with an incipient control actuator fault is proposed. This approach to proactive fault‐tolerant control combines the unique stability and robustness properties of LMPC as well as explicitly accounting for incipient control actuator faults in the formulation of the MPC. Our theoretical results are applied to a chemical process example, and different scenaria were simulated to demonstrate that the proposed proactive fault‐tolerant model predictive control method can achieve practical stability and efficiently deal with a control actuator fault.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Fault‐tolerant control methods have been extensively researched over the last 10 years in the context of chemical process control applications, and provide a natural framework for integrating process monitoring and control aspects in a way that not only fault detection and isolation but also control system reconfiguration is achieved in the event of a process or actuator fault. But almost all the efforts are focused on the reactive fault‐tolerant control. As another way for fault‐tolerant control, proactive fault‐tolerant control has been a popular topic in the communication systems and aerospace control systems communities for the last 10 years. At this point, no work has been done on proactive fault‐tolerant control within the context of chemical process control. Motivated by this, a proactive fault‐tolerant Lyapunov‐based model predictive controller (LMPC) that can effectively deal with an incipient control actuator fault is proposed. This approach to proactive fault‐tolerant control combines the unique stability and robustness properties of LMPC as well as explicitly accounting for incipient control actuator faults in the formulation of the MPC. Our theoretical results are applied to a chemical process example, and different scenaria were simulated to demonstrate that the proposed proactive fault‐tolerant model predictive control method can achieve practical stability and efficiently deal with a control actuator fault. |
Tu, T S; Ellis, M; Christofides, P D Model predictive control of a nonlinear large-scale process network used in the production of vinyl acetate Journal Article Industrial & Engineering Chemistry Research, 52 , pp. 12463–12481, 2013. @article{Tu201312463, title = {Model predictive control of a nonlinear large-scale process network used in the production of vinyl acetate}, author = {T S Tu and M Ellis and P D Christofides}, url = {http://pubs.acs.org/doi/abs/10.1021/ie400614t}, doi = {10.1021/ie400614t}, year = {2013}, date = {2013-01-01}, journal = {Industrial & Engineering Chemistry Research}, volume = {52}, pages = {12463--12481}, abstract = {In this work, we focus on the development and application of two Lyapunov-based model predictive control (LMPC) schemes to a large-scale nonlinear chemical process network used in the production of vinyl acetate. The nonlinear dynamic model of the process consists of 179 state variables and 13 control (manipulated) inputs and features a cooled plug-flow reactor, an eight-stage gas–liquid absorber, and both gas and liquid recycle streams. The two control schemes considered are an LMPC scheme which is formulated with a convectional quadratic cost function and a Lyapunov-based economic model predictive control (LEMPC) scheme which is formulated with an economic (nonquadratic) cost measure. The economic cost measure for the entire process network accounts for the reaction selectivity and the product separation quality. In the LMPC and LEMPC control schemes, five inputs, directly affecting the economic cost, are regulated with LMPC/LEMPC and the remaining eight inputs are computed by proportional–integral controllers. Simulations are carried out to study the economic performance of the closed-loop system under LMPC and under LEMPC formulated with the proposed economic measure. A thorough comparison of the two control schemes is provided.}, keywords = {}, pubstate = {published}, tppubtype = {article} } In this work, we focus on the development and application of two Lyapunov-based model predictive control (LMPC) schemes to a large-scale nonlinear chemical process network used in the production of vinyl acetate. The nonlinear dynamic model of the process consists of 179 state variables and 13 control (manipulated) inputs and features a cooled plug-flow reactor, an eight-stage gas–liquid absorber, and both gas and liquid recycle streams. The two control schemes considered are an LMPC scheme which is formulated with a convectional quadratic cost function and a Lyapunov-based economic model predictive control (LEMPC) scheme which is formulated with an economic (nonquadratic) cost measure. The economic cost measure for the entire process network accounts for the reaction selectivity and the product separation quality. In the LMPC and LEMPC control schemes, five inputs, directly affecting the economic cost, are regulated with LMPC/LEMPC and the remaining eight inputs are computed by proportional–integral controllers. Simulations are carried out to study the economic performance of the closed-loop system under LMPC and under LEMPC formulated with the proposed economic measure. A thorough comparison of the two control schemes is provided. |
Conference Proceedings
2023 |
dela Rosa, L; Mande, C; Ellis, M J Supervisory multi-objective economic model predictive control for heat pump water heaters for cost and carbon optimization Inproceedings Forthcoming Proceedings of the ASHRAE Conference, Forthcoming. @inproceedings{delaRosa2023ASHRAE, title = {Supervisory multi-objective economic model predictive control for heat pump water heaters for cost and carbon optimization}, author = {L. dela Rosa and C. Mande and M. J. Ellis}, year = {2023}, date = {2023-03-01}, booktitle = {Proceedings of the ASHRAE Conference}, abstract = {Heat pump water heaters (HPWHs) offer an efficient way to heat water using electricity, which aligns with efforts toward decarbonization and better utilization of renewable energy sources. Economic model predictive control (EMPC) provides an automated way to provide load flexibility of this new electric load by accounting for exogenous inputs like time-varying electric rates or marginal greenhouse gas emissions. Furthermore, a cloud-based supervisory controller implementation of EMPC enables retrofitting existing HPWHs with cloud-connection. In this work, the formulation of a supervisory multi-objective EMPC for HPWH is presented. The formulation of a supervisory multi-objective EMPC for HPWH is presented for equipment with a single heat pump and up to two backup resistive heating elements. A temperature setpoint is computed from the EMPC decisions using a logic-based setpoint calculator, so the existing HPWH rule-based control (RBC) strategy activates the desired heat sources when deemed optimal by the EMPC. The performance of the simulated testing results under the supervisory EMPC is compared against the performance under an RBC strategy and under a regulatory EMPC that directly controls the HPWH. The simulation results demonstrate that the RBC in the proposed control architecture, operates the HPWH heat sources in the optimal manner computed by the EMPC, which indicates that the setpoint calculator can translate EMPC decisions to appropriate temperature setpoints. To minimize heat pump cycling, the effect of increasing the minimum on-time for the heat pump is also considered, and the results show that increasing the minimum on-time increases the operating cost.}, keywords = {}, pubstate = {forthcoming}, tppubtype = {inproceedings} } Heat pump water heaters (HPWHs) offer an efficient way to heat water using electricity, which aligns with efforts toward decarbonization and better utilization of renewable energy sources. Economic model predictive control (EMPC) provides an automated way to provide load flexibility of this new electric load by accounting for exogenous inputs like time-varying electric rates or marginal greenhouse gas emissions. Furthermore, a cloud-based supervisory controller implementation of EMPC enables retrofitting existing HPWHs with cloud-connection. In this work, the formulation of a supervisory multi-objective EMPC for HPWH is presented. The formulation of a supervisory multi-objective EMPC for HPWH is presented for equipment with a single heat pump and up to two backup resistive heating elements. A temperature setpoint is computed from the EMPC decisions using a logic-based setpoint calculator, so the existing HPWH rule-based control (RBC) strategy activates the desired heat sources when deemed optimal by the EMPC. The performance of the simulated testing results under the supervisory EMPC is compared against the performance under an RBC strategy and under a regulatory EMPC that directly controls the HPWH. The simulation results demonstrate that the RBC in the proposed control architecture, operates the HPWH heat sources in the optimal manner computed by the EMPC, which indicates that the setpoint calculator can translate EMPC decisions to appropriate temperature setpoints. To minimize heat pump cycling, the effect of increasing the minimum on-time for the heat pump is also considered, and the results show that increasing the minimum on-time increases the operating cost. |
2022 |
Mande, C; Aboud, A; dela Rosa, L; Collins, R; Outcault, S; Ellis, M J Timing is everything: Optimizing load flexibility of heat pump water heaters for cost, comfort, and carbon emissions Inproceedings Proceedings of the ACEEE Summer Study on Energy Efficiency in Buildings, 2022. @inproceedings{Mande2022, title = {Timing is everything: Optimizing load flexibility of heat pump water heaters for cost, comfort, and carbon emissions}, author = {C. Mande and A. Aboud and L. dela Rosa and R. Collins and S. Outcault and M. J. Ellis}, year = {2022}, date = {2022-08-26}, booktitle = {Proceedings of the ACEEE Summer Study on Energy Efficiency in Buildings}, abstract = {As California continues to decarbonize the electrical grid and more customers electrify, load flexibility among heat pumps is becoming critical for maximizing the use of carbon-free electricity sources, stabilizing the electricity grid, and minimizing operating costs to end-users. The transition to all-electric housing has many concerned about potential increases in utility costs. Load flexibility controls offer a way to mitigate the impact of electrification on customers by shifting consumption to times of day with lower rates without compromising their comfort. Heat pump water heaters (HPWHs) are currently controlled using rule-based logic to maintain a programmed water temperature setpoint. This type of control usually does not provide any flexibility to when the heat pump operates. Economic model predictive control (MPC) is an advanced control technique that can provide automated load flexibility due to its ability to account for time-varying electric tariffs and available energy storage. A new configurable control framework is motivated and described to address the challenges of configuring economic MPC for deployment. This framework utilizes a graph-based system representation of the physical system that automatically instantiates the underlying economic MPC problem from the system representation and requires minimum MPC expertise. In this work, the MPC framework is described and applied to a simulated HPWH. The closed-loop simulation results are compared to the results obtained from simulations of an HPWH under a rule-based control approach.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } As California continues to decarbonize the electrical grid and more customers electrify, load flexibility among heat pumps is becoming critical for maximizing the use of carbon-free electricity sources, stabilizing the electricity grid, and minimizing operating costs to end-users. The transition to all-electric housing has many concerned about potential increases in utility costs. Load flexibility controls offer a way to mitigate the impact of electrification on customers by shifting consumption to times of day with lower rates without compromising their comfort. Heat pump water heaters (HPWHs) are currently controlled using rule-based logic to maintain a programmed water temperature setpoint. This type of control usually does not provide any flexibility to when the heat pump operates. Economic model predictive control (MPC) is an advanced control technique that can provide automated load flexibility due to its ability to account for time-varying electric tariffs and available energy storage. A new configurable control framework is motivated and described to address the challenges of configuring economic MPC for deployment. This framework utilizes a graph-based system representation of the physical system that automatically instantiates the underlying economic MPC problem from the system representation and requires minimum MPC expertise. In this work, the MPC framework is described and applied to a simulated HPWH. The closed-loop simulation results are compared to the results obtained from simulations of an HPWH under a rule-based control approach. |
Narasimhan, S; El-Farra, N H; Ellis, M J Cyberattack detectability-based controller screening: Application to a nonlinear process Inproceedings Proceedings of the 14th International Symposium on Process Systems Engineering, pp. 1453-1458, Kyoto, JP, 2022. @inproceedings{Narasimhan20221453, title = {Cyberattack detectability-based controller screening: Application to a nonlinear process}, author = {S. Narasimhan and N. H. El-Farra and M. J. Ellis}, doi = {10.1016/B978-0-323-85159-6.50242-6}, year = {2022}, date = {2022-06-23}, booktitle = {Proceedings of the 14th International Symposium on Process Systems Engineering}, pages = {1453-1458}, address = {Kyoto, JP}, abstract = {In this work, multiplicative cyberattacks targeting the sensor-controller communication link of a process control system are considered. The interdependence of detectability of an attack with respect to a general class of residual-based detection schemes and the control parameters is characterized. Exploiting this dependence, a controller screening methodology that may be used to incorporate cyberattack detectability into the standard controller design criteria is presented. Using a chemical process example, the application of the controller design screening to a nonlinear process is demonstrated.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } In this work, multiplicative cyberattacks targeting the sensor-controller communication link of a process control system are considered. The interdependence of detectability of an attack with respect to a general class of residual-based detection schemes and the control parameters is characterized. Exploiting this dependence, a controller screening methodology that may be used to incorporate cyberattack detectability into the standard controller design criteria is presented. Using a chemical process example, the application of the controller design screening to a nonlinear process is demonstrated. |
Narasimhan, S; El-Farra, N H; Ellis, M J Controller switching-enabled active detection of multiplicative cyberattacks on process systems Inproceedings Proceedings of the American Control Conference, pp. 2473-2478, Atlanta, GA, 2022. @inproceedings{Narasimhan20222473, title = {Controller switching-enabled active detection of multiplicative cyberattacks on process systems}, author = {S. Narasimhan and N. H. El-Farra and M. J. Ellis}, doi = {10.23919/ACC53348.2022.9867804}, year = {2022}, date = {2022-06-10}, booktitle = {Proceedings of the American Control Conference}, pages = {2473-2478}, address = {Atlanta, GA}, abstract = {This work focuses on the problem of enhancing cyberattack detection capabilities in process control systems subject to multiplicative cyberattacks. First, the relationship between closed-loop stability and attack detectability with respect to a class of residual-based detection schemes is rigorously analyzed. The results are used to identify a set of controller parameters (called "attack-sensitive" controller parameters) under which an attack can destabilize the closed-loop system. The selection of attack-sensitive controller parameters can enhance the ability to detect attacks, but can also degrade the performance of the attack-free closed-loop system. To balance this trade-off, a novel active attack detection methodology employing controller parameter switching between the nominal controller parameters (chosen on the basis of standard control design criteria) and the attack-sensitive controller parameters, is developed. The proposed methodology is applied to a chemical process example to demonstrate its ability to detect multiplicative sensor-controller communication link attacks.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } This work focuses on the problem of enhancing cyberattack detection capabilities in process control systems subject to multiplicative cyberattacks. First, the relationship between closed-loop stability and attack detectability with respect to a class of residual-based detection schemes is rigorously analyzed. The results are used to identify a set of controller parameters (called "attack-sensitive" controller parameters) under which an attack can destabilize the closed-loop system. The selection of attack-sensitive controller parameters can enhance the ability to detect attacks, but can also degrade the performance of the attack-free closed-loop system. To balance this trade-off, a novel active attack detection methodology employing controller parameter switching between the nominal controller parameters (chosen on the basis of standard control design criteria) and the attack-sensitive controller parameters, is developed. The proposed methodology is applied to a chemical process example to demonstrate its ability to detect multiplicative sensor-controller communication link attacks. |
2021 |
Ellis, M J Machine learning enhanced grey-box models for building thermal modeling Inproceedings Proceedings of the American Control Conference, pp. 3971-3922, New Orleans, LA, 2021. @inproceedings{Ellis20213971, title = {Machine learning enhanced grey-box models for building thermal modeling}, author = {M. J. Ellis}, year = {2021}, date = {2021-05-28}, booktitle = {Proceedings of the American Control Conference}, pages = {3971-3922}, address = {New Orleans, LA}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Ellis, M J; Chinde, V Recurrent neural network-based economic MPC applied to building HVAC systems Inproceedings Proceedings of the 6th International High Performance Buildings Conference, pp. 3649, West Lafayette, IN, 2021. @inproceedings{Ellis20213649, title = {Recurrent neural network-based economic MPC applied to building HVAC systems}, author = {M. J. Ellis and V. Chinde}, year = {2021}, date = {2021-05-28}, booktitle = {Proceedings of the 6th International High Performance Buildings Conference}, pages = {3649}, address = {West Lafayette, IN}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Frasier, A; Meyers, F; Ross, D; Pistochini, T; Ellis, M J A standard testing protocol applied to CO2 Sensors and CO2-based demand control ventilation systems Inproceedings Proceedings of the 6th International High Performance Buildings Conference, pp. 3649, West Lafayette, IN, 2021. @inproceedings{Frasier2021310071, title = {A standard testing protocol applied to CO2 Sensors and CO2-based demand control ventilation systems}, author = {A. Frasier and F. Meyers and D. Ross and T. Pistochini and M. J. Ellis}, year = {2021}, date = {2021-05-28}, booktitle = {Proceedings of the 6th International High Performance Buildings Conference}, pages = {3649}, address = {West Lafayette, IN}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
2020 |
Ellis, M J Formulation of economic model predictive control to address system dynamics over multiple time scales Inproceedings Proceedings of the American Control Conference, pp. 1974–1979, Denver, CO, 2020. @inproceedings{Ellis20201974, title = {Formulation of economic model predictive control to address system dynamics over multiple time scales}, author = {M J Ellis}, year = {2020}, date = {2020-07-01}, booktitle = {Proceedings of the American Control Conference}, pages = {1974--1979}, address = {Denver, CO}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
2018 |
Lenhardt, B; Ellis, M J; Turney, R D Economic model predictive control for variable refrigerant flow systems Inproceedings Proceedings of the 5th International High Performance Buildings Conference, pp. 2125, West Lafayette, IN, 2018. @inproceedings{Lenhardt20182125, title = {Economic model predictive control for variable refrigerant flow systems}, author = {B Lenhardt and M J Ellis and R D Turney}, year = {2018}, date = {2018-01-01}, booktitle = {Proceedings of the 5th International High Performance Buildings Conference}, pages = {2125}, address = {West Lafayette, IN}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Patel, N R; Rawlings, J B; Ellis, M J; Wenzel, M J; Turney, R D An economic model predictive control framework for distributed embedded battery applications Inproceedings Proceedings of the 5th International High Performance Buildings Conference, pp. 3134, West Lafayette, IN, 2018. @inproceedings{Patel20183134, title = {An economic model predictive control framework for distributed embedded battery applications}, author = {N R Patel and J B Rawlings and M J Ellis and M J Wenzel and R D Turney}, year = {2018}, date = {2018-01-01}, booktitle = {Proceedings of the 5th International High Performance Buildings Conference}, pages = {3134}, address = {West Lafayette, IN}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Alanqar, A; Ellis, M J; Bernal, Tapiero J E; Wenzel, M J Practice-oriented system identification strategies for MPC of building thermal and HVAC dynamics Inproceedings Proceedings of the 5th International High Performance Buildings Conference, pp. 3142, West Lafayette, IN, 2018. @inproceedings{Alanqar20183142, title = {Practice-oriented system identification strategies for MPC of building thermal and HVAC dynamics}, author = {A Alanqar and M J Ellis and Tapiero J E Bernal and M J Wenzel}, year = {2018}, date = {2018-01-01}, booktitle = {Proceedings of the 5th International High Performance Buildings Conference}, pages = {3142}, address = {West Lafayette, IN}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Kumar, R; Wenzel, M J; Ellis, M J; ElBsat, M N; Drees, K H; Zavala, V W A hierarchical model predictive control approch to battery systems Inproceedings Proceedings of the 5th International High Performance Buildings Conference, pp. 3186, West Lafayette, IN, 2018. @inproceedings{Kumar20183186, title = {A hierarchical model predictive control approch to battery systems}, author = {R Kumar and M J Wenzel and M J Ellis and M N ElBsat and K H Drees and V W Zavala}, year = {2018}, date = {2018-01-01}, booktitle = {Proceedings of the 5th International High Performance Buildings Conference}, pages = {3186}, address = {West Lafayette, IN}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Ellis, M J; Alanqar, A Formulation and application of an economic model predictive control scheme for connected thermostats Inproceedings Proceedings of the 5th International High Performance Buildings Conference, pp. 3194, West Lafayette, IN, 2018. @inproceedings{Ellis20183194, title = {Formulation and application of an economic model predictive control scheme for connected thermostats}, author = {M J Ellis and A Alanqar}, year = {2018}, date = {2018-01-01}, booktitle = {Proceedings of the 5th International High Performance Buildings Conference}, pages = {3194}, address = {West Lafayette, IN}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Wenzel, M J; ElBsat, M N; Ellis, M J; Asmus, M J; Przybyiski, A J; Baumgartner, R; Burroughs, J H; Willmott, G; Drees, K H; Turney, R D Large scale optimization problems for central energy facilities with distributed energy storage Inproceedings Proceedings of the 5th International High Performance Buildings Conference, pp. 3560, West Lafayette, IN, 2018. @inproceedings{Wenzel20183560, title = {Large scale optimization problems for central energy facilities with distributed energy storage}, author = {M J Wenzel and M N ElBsat and M J Ellis and M J Asmus and A J Przybyiski and R Baumgartner and J H Burroughs and G Willmott and K H Drees and R D Turney}, year = {2018}, date = {2018-01-01}, booktitle = {Proceedings of the 5th International High Performance Buildings Conference}, pages = {3560}, address = {West Lafayette, IN}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
2016 |
Ellis, M; Christofides, P D On closed-loop average economic performance under Lyapunov-based economic model predictive control Inproceedings Proceedings of the American Control Conference, pp. 4488–4493, Boston, MA, 2016. @inproceedings{Ellis20164488, title = {On closed-loop average economic performance under Lyapunov-based economic model predictive control}, author = {M Ellis and P D Christofides}, year = {2016}, date = {2016-01-01}, booktitle = {Proceedings of the American Control Conference}, pages = {4488--4493}, address = {Boston, MA}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Ellis, M J; Wenzel, M J; Turney, R D System identification for model predictive control of building region temperature Inproceedings Proceedings of the 4th International High Performance Buildings Conference, pp. 3583, West Lafayette, IN, 2016. @inproceedings{Ellis20163583, title = {System identification for model predictive control of building region temperature}, author = {M J Ellis and M J Wenzel and R D Turney}, year = {2016}, date = {2016-01-01}, booktitle = {Proceedings of the 4th International High Performance Buildings Conference}, pages = {3583}, address = {West Lafayette, IN}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Kumar, R; Wenzel, M J; Ellis, M J; ElBsat, M N; Drees, K H; Zavala, V M Handling long horizons in MPC: A stochastic programming approach Inproceedings Proceedings of the American Control Conference, pp. 4488–4493, Boston, MA, 2016. @inproceedings{Kumar20184488, title = {Handling long horizons in MPC: A stochastic programming approach}, author = {R Kumar and M J Wenzel and M J Ellis and M N ElBsat and K H Drees and V M Zavala}, year = {2016}, date = {2016-01-01}, booktitle = {Proceedings of the American Control Conference}, pages = {4488--4493}, address = {Boston, MA}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
2015 |
Anderson, T; Ellis, M; Christofides, P D Distributed economic model predictive control of a catalytic reactor: Evaluation of sequential and iterative architectures Inproceedings Proceedings of the IFAC International Symposium on Advanced Control of Chemical Processes, pp. 278–283, Whistler, BC, 2015. @inproceedings{Anderson2015278, title = {Distributed economic model predictive control of a catalytic reactor: Evaluation of sequential and iterative architectures}, author = {T Anderson and M Ellis and P D Christofides}, year = {2015}, date = {2015-01-01}, booktitle = {Proceedings of the IFAC International Symposium on Advanced Control of Chemical Processes}, pages = {278--283}, address = {Whistler, BC}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Alanqar, A; Ellis, M; Christofides, P D Economic model predictive control of nonlinear process systems using multiple empirical models Inproceedings Proceedings of the American Control Conference, pp. 4953–4958, Chicago, IL, 2015. @inproceedings{Alanqar20154953, title = {Economic model predictive control of nonlinear process systems using multiple empirical models}, author = {A Alanqar and M Ellis and P D Christofides}, doi = {10.1109/ACC.2015.7172110}, year = {2015}, date = {2015-01-01}, booktitle = {Proceedings of the American Control Conference}, pages = {4953--4958}, address = {Chicago, IL}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Durand, H; Ellis, M; Christofides, P D Accounting for the control actuator layer in economic model predictive control of nonlinear processes Inproceedings Proceedings of the American Control Conference, pp. 2968–2973, Chicago, IL, 2015. @inproceedings{Durand20152968, title = {Accounting for the control actuator layer in economic model predictive control of nonlinear processes}, author = {H Durand and M Ellis and P D Christofides}, doi = {10.1109/ACC.2015.7171186}, year = {2015}, date = {2015-01-01}, booktitle = {Proceedings of the American Control Conference}, pages = {2968--2973}, address = {Chicago, IL}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Messori, M; Ellis, M; Cobelli, C; Christofides, P D; Magni, L Improved postprandial glucose control with a customized model predictive controller Inproceedings Proceedings of the American Control Conference, pp. 5108–5115, Chicago, IL, 2015. @inproceedings{Messori20155108, title = {Improved postprandial glucose control with a customized model predictive controller}, author = {M Messori and M Ellis and C Cobelli and P D Christofides and L Magni}, doi = {10.1109/ACC.2015.7172136}, year = {2015}, date = {2015-01-01}, booktitle = {Proceedings of the American Control Conference}, pages = {5108--5115}, address = {Chicago, IL}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Ellis, M; Christofides, P D Handling computational delay in economic model predictive control of nonlinear process systems Inproceedings Proceedings of the American Control Conference, pp. 2962–2967, Chicago, IL, 2015. @inproceedings{Ellis20152962, title = {Handling computational delay in economic model predictive control of nonlinear process systems}, author = {M Ellis and P D Christofides}, doi = {10.1109/ACC.2015.7171185}, year = {2015}, date = {2015-01-01}, booktitle = {Proceedings of the American Control Conference}, pages = {2962--2967}, address = {Chicago, IL}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Ellis, M; Christofides, P D Economic model predictive control: Elucidation of the role of constraints Inproceedings Proceedings of 5th IFAC Conference on Nonlinear Model Predictive Control, pp. 47–56, Seville, Spain, 2015. @inproceedings{Ellis201547, title = {Economic model predictive control: Elucidation of the role of constraints}, author = {M Ellis and P D Christofides}, year = {2015}, date = {2015-01-01}, booktitle = {Proceedings of 5th IFAC Conference on Nonlinear Model Predictive Control}, pages = {47--56}, address = {Seville, Spain}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
2014 |
Lao, L; Ellis, M; Christofides, P D Output feedback economic model predictive control of parabolic PDE systems Inproceedings Proceedings of the American Control Conference, pp. 1655–1660, Portland, OR, 2014, ISSN: 0743-1619. @inproceedings{Lao20141655, title = {Output feedback economic model predictive control of parabolic PDE systems}, author = {L Lao and M Ellis and P D Christofides}, doi = {10.1109/ACC.2014.6858762}, issn = {0743-1619}, year = {2014}, date = {2014-01-01}, booktitle = {Proceedings of the American Control Conference}, pages = {1655--1660}, address = {Portland, OR}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Lao, L; Ellis, M; Armaou, A; Christofides, P D Economic model predictive control of parabolic PDE systems using empirical eigenfunctions Inproceedings Proceedings of the American Control Conference, pp. 3375–3380, Portland, OR, 2014, ISSN: 0743-1619. @inproceedings{Lao20143375, title = {Economic model predictive control of parabolic PDE systems using empirical eigenfunctions}, author = {L Lao and M Ellis and A Armaou and P D Christofides}, doi = {10.1109/ACC.2014.6858761}, issn = {0743-1619}, year = {2014}, date = {2014-01-01}, booktitle = {Proceedings of the American Control Conference}, pages = {3375--3380}, address = {Portland, OR}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Ellis, M; Karafyllis, I; Christofides, P D Stabilization of nonlinear sampled-data systems and economic model predictive control application Inproceedings Proceedings of the American Control Conference, pp. 5594–5601, Portland, OR, 2014, ISSN: 0743-1619. @inproceedings{Ellis20145594, title = {Stabilization of nonlinear sampled-data systems and economic model predictive control application}, author = {M Ellis and I Karafyllis and P D Christofides}, doi = {10.1109/ACC.2014.6858758}, issn = {0743-1619}, year = {2014}, date = {2014-01-01}, booktitle = {Proceedings of the American Control Conference}, pages = {5594--5601}, address = {Portland, OR}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Lao, L; Ellis, M; Christofides, P D Economic model predictive control of a first-order hyperbolic PDE system Inproceedings Proceedings of the 53rd IEEE Conference on Decision and Control, pp. 563–570, Los Angeles, CA, 2014. @inproceedings{Lao2014563, title = {Economic model predictive control of a first-order hyperbolic PDE system}, author = {L Lao and M Ellis and P D Christofides}, doi = {10.1109/CDC.2014.7039441}, year = {2014}, date = {2014-01-01}, booktitle = {Proceedings of the 53rd IEEE Conference on Decision and Control}, pages = {563--570}, address = {Los Angeles, CA}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Ellis, M; Christofides, P D Control configuration selection for economic model predictive control Inproceedings Proceedings of the 53rd IEEE Conference on Decision and Control, pp. 789–796, 2014. @inproceedings{Ellis2014789, title = {Control configuration selection for economic model predictive control}, author = {M Ellis and P D Christofides}, doi = {10.1109/CDC.2014.7039478}, year = {2014}, date = {2014-01-01}, booktitle = {Proceedings of the 53rd IEEE Conference on Decision and Control}, pages = {789--796}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Lao, L; Ellis, M; Armaou, A; Christofides, P D Proceedings of the 53rd IEEE Conference on Decision and Control, pp. 2758–2763, Los Angeles, CA, 2014. @inproceedings{Lao20142758, title = {Economic model predictive control of parabolic PDE systems: Handling state constraints by adaptive proper orthogonal decomposition}, author = {L Lao and M Ellis and A Armaou and P D Christofides}, doi = {10.1109/CDC.2014.7039812}, year = {2014}, date = {2014-01-01}, booktitle = {Proceedings of the 53rd IEEE Conference on Decision and Control}, pages = {2758--2763}, address = {Los Angeles, CA}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
2013 |
Lao, L; Ellis, M; Christofides, P D Proactive fault-tolerant model predictive control: Concept and application Inproceedings Proceedings of the American Control Conference, pp. 5140–5145, Washington, D.C., 2013, ISSN: 0743-1619. @inproceedings{Lao20135140, title = {Proactive fault-tolerant model predictive control: Concept and application}, author = {L Lao and M Ellis and P D Christofides}, doi = {10.1109/ACC.2013.6580637}, issn = {0743-1619}, year = {2013}, date = {2013-01-01}, booktitle = {Proceedings of the American Control Conference}, pages = {5140--5145}, address = {Washington, D.C.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Ellis, M; Heidarinejad, M; Christofides, P D Economic model predictive control of nonlinear two-time-scale systems Inproceedings Proceedings of the 21st IEEE Mediterranean Conference on Control and Automation, pp. 323–328, Platanias-Chania, Crete, Greece, 2013. @inproceedings{Ellis2013323, title = {Economic model predictive control of nonlinear two-time-scale systems}, author = {M Ellis and M Heidarinejad and P D Christofides}, year = {2013}, date = {2013-01-01}, booktitle = {Proceedings of the 21st IEEE Mediterranean Conference on Control and Automation}, pages = {323--328}, address = {Platanias-Chania, Crete, Greece}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Lao, L; Ellis, M; Christofides, P D Economic model predictive control of a transport-reaction process Inproceedings Proceedings of the 21st IEEE Mediterranean Conference on Control and Automation, pp. 329–334, Platanias-Chania, Crete, Greece, 2013. @inproceedings{Lao2013329, title = {Economic model predictive control of a transport-reaction process}, author = {L Lao and M Ellis and P D Christofides}, year = {2013}, date = {2013-01-01}, booktitle = {Proceedings of the 21st IEEE Mediterranean Conference on Control and Automation}, pages = {329--334}, address = {Platanias-Chania, Crete, Greece}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Patents
2022 |
Turney, R D; Wenzel, M J; ElBsat, M N; Yang, L; Ellis, M J; Nonaka, M Systems and methods for maintaining occupant comfort for various environmental conditions Patent 2022, (US Non-provisional Patent Number: 11,281,173). @patent{Turney202211281173, title = {Systems and methods for maintaining occupant comfort for various environmental conditions}, author = {R D Turney and M J Wenzel and M N ElBsat and L Yang and M J Ellis and M Nonaka}, url = {http://patft1.uspto.gov/netacgi/nph-Parser?patentnumber=11281173}, year = {2022}, date = {2022-12-01}, note = {US Non-provisional Patent Number: 11,281,173}, keywords = {}, pubstate = {published}, tppubtype = {patent} } |
Wenzel, M J; Turney, R D; Li, J; Ellis, M J; ElBsat, M N 2022, (US Non-provisional Patent Number: 11,385,605). @patent{Wenzel202211385605, title = {Building control system with features for operating under intermittent connectivity to a cloud computation system}, author = {M J Wenzel and R D Turney and J Li and M J Ellis and M N ElBsat}, url = {http://patft1.uspto.gov/netacgi/nph-Parser?patentnumber=11385605}, year = {2022}, date = {2022-11-01}, note = {US Non-provisional Patent Number: 11,385,605}, keywords = {}, pubstate = {published}, tppubtype = {patent} } |
Alanqar, A W I; Du, F; Wenzel, M J; Ellis, M J; ElBsat, M N Building control system with heat disturbance estimation and prediction Patent 2022, (US Non-provisional Patent Number: 11,215,375). @patent{Alanqar202211215375, title = {Building control system with heat disturbance estimation and prediction}, author = {A W I Alanqar and F Du and M J Wenzel and M J Ellis and M N ElBsat}, url = {http://patft1.uspto.gov/netacgi/nph-Parser?patentnumber=11215375}, year = {2022}, date = {2022-10-01}, note = {US Non-provisional Patent Number: 11,215,375}, keywords = {}, pubstate = {published}, tppubtype = {patent} } |
Bell, K M; Kapler, B E; Schwegler, A S; Mousavi, L; Robbins, K R; Turney, R D; Ellis, M J; Wenzel, M J; ElBsat, M N; Bernal, Tapiero J E; Fentzlaff, B H Smart thermostat with model predictive control and demand response integration Patent 2022, (US Non-provisional Patent Number: 11,274,849). @patent{Bell202211274849, title = {Smart thermostat with model predictive control and demand response integration}, author = {K M Bell and B E Kapler and A S Schwegler and L Mousavi and K R Robbins and R D Turney and M J Ellis and M J Wenzel and M N ElBsat and Tapiero J E Bernal and B H Fentzlaff}, url = {http://patft1.uspto.gov/netacgi/nph-Parser?patentnumber=11274849}, year = {2022}, date = {2022-09-01}, note = {US Non-provisional Patent Number: 11,274,849}, keywords = {}, pubstate = {published}, tppubtype = {patent} } |
Turney, R D; ElBsat, M N; Ellis, M J; Alanqar, A W I; Wenzel, M J Building control system with automatic comfort constraint generation Patent 2022, (US Non-provisional Patent Number: 11,415,334). @patent{Turney202211415334, title = {Building control system with automatic comfort constraint generation}, author = {R. D. Turney and M. N. ElBsat and M. J. Ellis and A. W. I. Alanqar and M. J. Wenzel}, year = {2022}, date = {2022-08-16}, note = {US Non-provisional Patent Number: 11,415,334}, keywords = {}, pubstate = {published}, tppubtype = {patent} } |
Przybylski, A J; Wenzel, M J; Ellis, M J Building management system with online configurable system identification Patent 2022, (US Non-provisional Patent Number: 11,243,503). @patent{Przybylski202211243503, title = {Building management system with online configurable system identification}, author = {A J Przybylski and M J Wenzel and M J Ellis}, url = {http://patft1.uspto.gov/netacgi/nph-Parser?patentnumber=11243503}, year = {2022}, date = {2022-07-01}, note = {US Non-provisional Patent Number: 11,243,503}, keywords = {}, pubstate = {published}, tppubtype = {patent} } |
Przybylski, A J; Wenzel, M J; Ellis, M J; Mueller, J T Central plant control system with plug and play EMPC Patent 2022, (US Non-provisional Patent Number: 11,275,363). @patent{Przybylski202211275363, title = {Central plant control system with plug and play EMPC}, author = {A J Przybylski and M J Wenzel and M J Ellis and J T Mueller}, url = {http://patft1.uspto.gov/netacgi/nph-Parser?patentnumber=11275363}, year = {2022}, date = {2022-06-01}, note = {US Non-provisional Patent Number: 11,275,363}, keywords = {}, pubstate = {published}, tppubtype = {patent} } |
Fread, J W; Willmott, G; Beaty, R C; Schlagenhaft, S A; Ellis, M J; Wenzel, M J Central plant optimization with optimization modification Patent 2022, (US Non-provisional Patent Number: 11,281,198). @patent{Fread202211281198, title = {Central plant optimization with optimization modification}, author = {J W Fread and G Willmott and R C Beaty and S A Schlagenhaft and M J Ellis and M J Wenzel}, url = {http://patft1.uspto.gov/netacgi/nph-Parser?patentnumber=11281198}, year = {2022}, date = {2022-05-01}, note = {US Non-provisional Patent Number: 11,281,198}, keywords = {}, pubstate = {published}, tppubtype = {patent} } |
2021 |
Wenzel, M J; Ellis, M J Central plant with asset allocator Patent 2021, (US Non-provisional Patent Number: 10,956,842). @patent{Wenzel202110956842, title = {Central plant with asset allocator}, author = {M J Wenzel and M J Ellis}, url = {http://patft1.uspto.gov/netacgi/nph-Parser?patentnumber=10956842}, year = {2021}, date = {2021-10-01}, note = {US Non-provisional Patent Number: 10,956,842}, keywords = {}, pubstate = {published}, tppubtype = {patent} } |
Ellis, M J; Burroughs, J H; Bernal, Tapiero J E; Alanqar, A W I Building management system with automatic comfort constraint adjustment Patent 2021, (US Non-provisional Patent Number: 11,098,921). @patent{Ellis202111098921, title = {Building management system with automatic comfort constraint adjustment}, author = {M J Ellis and J H Burroughs and Tapiero J E Bernal and A W I Alanqar}, url = {http://patft1.uspto.gov/netacgi/nph-Parser?patentnumber=11098921}, year = {2021}, date = {2021-07-01}, note = {US Non-provisional Patent Number: 11,098,921}, keywords = {}, pubstate = {published}, tppubtype = {patent} } |
Ellis, M J; Alanqar, A W I Building management system with triggered feedback set-point signal for persistent excitation Patent 2021, (US Non-provisional Patent Number: 11,085,663). @patent{Ellis202111085663, title = {Building management system with triggered feedback set-point signal for persistent excitation}, author = {M J Ellis and A W I Alanqar}, url = {http://patft1.uspto.gov/netacgi/nph-Parser?patentnumber=11085663}, year = {2021}, date = {2021-07-01}, note = {US Non-provisional Patent Number: 11,085,663}, keywords = {}, pubstate = {published}, tppubtype = {patent} } |
Alanqar, A W I; Ellis, M J Building control system with automated Kalman filter parameter initiation and system identification Patent 2021, (US Non-provisional Patent Number: 11,210,591). @patent{Alanqar202111210591, title = {Building control system with automated Kalman filter parameter initiation and system identification}, author = {A W I Alanqar and M J Ellis}, url = {http://patft1.uspto.gov/netacgi/nph-Parser?patentnumber=11210591}, year = {2021}, date = {2021-06-01}, note = {US Non-provisional Patent Number: 11,210,591}, keywords = {}, pubstate = {published}, tppubtype = {patent} } |
Burroughs, J H; Przybylski, A J; Ellis, M J; ElBsat, M N; Wenzel, M J Variable refrigerant flow system with zone grouping Patent 2021, (US Non-provisional Patent Number: 11,137,162). @patent{Burroughs202111137162, title = {Variable refrigerant flow system with zone grouping}, author = {J H Burroughs and A J Przybylski and M J Ellis and M N ElBsat and M J Wenzel}, url = {http://patft1.uspto.gov/netacgi/nph-Parser?patentnumber=11137162}, year = {2021}, date = {2021-06-01}, note = {US Non-provisional Patent Number: 11,137,162}, keywords = {}, pubstate = {published}, tppubtype = {patent} } |
Burroughs, J H; Przybylski, A J; Wenzel, M J; Ellis, M J 2021, (US Non-provisional Patent Number: 10,955,800). @patent{Burroughs202110955800, title = {Central plant control system, method, and controller with multi-level granular and non-granular asset allocation}, author = {J H Burroughs and A J Przybylski and M J Wenzel and M J Ellis}, url = {http://patft1.uspto.gov/netacgi/nph-Parser?patentnumber=10955800}, year = {2021}, date = {2021-05-01}, note = {US Non-provisional Patent Number: 10,955,800}, keywords = {}, pubstate = {published}, tppubtype = {patent} } |
Wenzel, M J; ElBsat, M N; Ellis, M J; Przybylski, A J; Baumgartner, R A; Burroughs, J H; Drees, K H; Turney, R D Control system for central energy facility with distributed energy storage Patent 2021, (US Non-provisional Patent Number: 11,209,184). @patent{Wenzel202111209184, title = {Control system for central energy facility with distributed energy storage}, author = {M J Wenzel and M N ElBsat and M J Ellis and A J Przybylski and R A Baumgartner and J H Burroughs and K H Drees and R D Turney}, url = {http://patft1.uspto.gov/netacgi/nph-Parser?patentnumber=11209184}, year = {2021}, date = {2021-01-01}, note = {US Non-provisional Patent Number: 11,209,184}, keywords = {}, pubstate = {published}, tppubtype = {patent} } |
2020 |
Patel, N R; Turney, R D; Ellis, M J 2020, (US Non-provisional Patent Number: 10,809,675). @patent{Patel202010809675, title = {HVAC system using model predictive control system with distributed low-level airside optimization and airside power consumption model}, author = {N R Patel and R D Turney and M J Ellis}, url = {http://patft1.uspto.gov/netacgi/nph-Parser?patentnumber=10809675}, year = {2020}, date = {2020-10-01}, note = {US Non-provisional Patent Number: 10,809,675}, keywords = {}, pubstate = {published}, tppubtype = {patent} } |
Patel, N R; Ellis, M J; Wenzel, M J; Turney, R D; Lenhardt, B M Building HVAC system with multi-level model predictive control Patent 2020, (US Non-provisional Patent Number: 10,809,676). @patent{Patel202010809676, title = {Building HVAC system with multi-level model predictive control}, author = {N R Patel and M J Ellis and M J Wenzel and R D Turney and B M Lenhardt}, url = {http://patft1.uspto.gov/netacgi/nph-Parser?patentnumber=10809676}, year = {2020}, date = {2020-10-01}, note = {US Non-provisional Patent Number: 10,809,676}, keywords = {}, pubstate = {published}, tppubtype = {patent} } |
Wenzel, M J; Drees, K H; Ruiz, J I; Ellis, M J; ElBsat, M N; Burroughs, J H; Bernal, Tapiero J E Photovoltaic energy system with stationary storage control Patent 2020, (US Non-provisional Patent Number: 10,673,380). @patent{Wenzel202010673380, title = {Photovoltaic energy system with stationary storage control}, author = {M J Wenzel and K H Drees and J I Ruiz and M J Ellis and M N ElBsat and J H Burroughs and Tapiero J E Bernal}, url = {http://patft1.uspto.gov/netacgi/nph-Parser?patentnumber=10673380}, year = {2020}, date = {2020-09-01}, note = {US Non-provisional Patent Number: 10,673,380}, keywords = {}, pubstate = {published}, tppubtype = {patent} } |
Patel, N R; Ellis, M J; Wenzel, M J; Turney, R D; Lenhardt, B M Variable refrigerant flow system with multi-level model predictive control Patent 2020, (US Non-provisional Patent Number: 10,564,612). @patent{Patel202010564612, title = {Variable refrigerant flow system with multi-level model predictive control}, author = {N R Patel and M J Ellis and M J Wenzel and R D Turney and B M Lenhardt}, url = {http://patft1.uspto.gov/netacgi/nph-Parser?patentnumber=10564612}, year = {2020}, date = {2020-06-01}, note = {US Non-provisional Patent Number: 10,564,612}, keywords = {}, pubstate = {published}, tppubtype = {patent} } |
Kumar, R; Wenzel, M J; Ellis, M J; ElBsat, M N; Drees, K H; Tejeda, Zavala V M Building energy system with stochastic model predictive control Patent 2020, (US Non-provisional Patent Number: 10,739,742). @patent{Kumar202010739742, title = {Building energy system with stochastic model predictive control}, author = {R Kumar and M J Wenzel and M J Ellis and M N ElBsat and K H Drees and Zavala V M Tejeda}, url = {http://patft1.uspto.gov/netacgi/nph-Parser?patentnumber=10739742}, year = {2020}, date = {2020-04-01}, note = {US Non-provisional Patent Number: 10,739,742}, keywords = {}, pubstate = {published}, tppubtype = {patent} } |
Alanqar, A W I; Ellis, M J; Wenzel, M J; Bernal, Tapiero J E Building management system with system identification using multi-step ahead error prediction Patent 2020, (US Non-provisional Patent Number: 10,718,542). @patent{Alanqar202010718542, title = {Building management system with system identification using multi-step ahead error prediction}, author = {A W I Alanqar and M J Ellis and M J Wenzel and Tapiero J E Bernal}, url = {http://patft1.uspto.gov/netacgi/nph-Parser?patentnumber=10718542}, year = {2020}, date = {2020-04-01}, note = {US Non-provisional Patent Number: 10,718,542}, keywords = {}, pubstate = {published}, tppubtype = {patent} } |
Wenzel, M J; Ellis, M J Central plant with asset allocator Patent 2020, (US Non-provisional Patent Number: 10,706,375). @patent{Wenzel202010706375, title = {Central plant with asset allocator}, author = {M J Wenzel and M J Ellis}, url = {http://patft1.uspto.gov/netacgi/nph-Parser?patentnumber=10706375}, year = {2020}, date = {2020-03-01}, note = {US Non-provisional Patent Number: 10,706,375}, keywords = {}, pubstate = {published}, tppubtype = {patent} } |
Przybylski, A J; Wenzel, M J; Ellis, M J; Mueller, J T Central plant control system with plug and play EMPC Patent 2020, (US Non-provisional Patent Number: 10,678,227). @patent{Przybylski202010678227, title = {Central plant control system with plug and play EMPC}, author = {A J Przybylski and M J Wenzel and M J Ellis and J T Mueller}, url = {http://patft1.uspto.gov/netacgi/nph-Parser?patentnumber=10678227}, year = {2020}, date = {2020-03-01}, note = {US Non-provisional Patent Number: 10,678,227}, keywords = {}, pubstate = {published}, tppubtype = {patent} } |
Alanqar, A W I; Ellis, M J; Wenzel, M J Building management system with saturation detection and removal for system identification Patent 2020, (US Non-provisional Patent Number: 10,767,886). @patent{Alanqar202010767886, title = {Building management system with saturation detection and removal for system identification}, author = {A W I Alanqar and M J Ellis and M J Wenzel}, url = {http://patft1.uspto.gov/netacgi/nph-Parser?patentnumber=10767886}, year = {2020}, date = {2020-02-01}, note = {US Non-provisional Patent Number: 10,767,886}, keywords = {}, pubstate = {published}, tppubtype = {patent} } |
Przybylski, A J; Schluechtermann, T; Burroughs, J H; Mueller, J T; Wenzel, M J; Ellis, M J Building automation system with an energy optimization builder and generic data model designer Patent 2020, (US Non-provisional Patent Number: 10,767,885). @patent{Przybylski202010767885, title = {Building automation system with an energy optimization builder and generic data model designer}, author = {A J Przybylski and T Schluechtermann and J H Burroughs and J T Mueller and M J Wenzel and M J Ellis}, url = {http://patft1.uspto.gov/netacgi/nph-Parser?patentnumber=10767885}, year = {2020}, date = {2020-02-01}, note = {US Non-provisional Patent Number: 10,767,885}, keywords = {}, pubstate = {published}, tppubtype = {patent} } |
Patel, N R; Turney, R D; Ellis, M J HVAC system using model predictive control system with distributed low-level airside optimization Patent 2020, (US Non-provisional Patent Number: 11,067,955). @patent{Patel202011067955, title = {HVAC system using model predictive control system with distributed low-level airside optimization}, author = {N R Patel and R D Turney and M J Ellis}, url = {http://patft1.uspto.gov/netacgi/nph-Parser?patentnumber=11067955}, year = {2020}, date = {2020-01-01}, note = {US Non-provisional Patent Number: 11,067,955}, keywords = {}, pubstate = {published}, tppubtype = {patent} } |
Alanqar, A W I; Ellis, M J; Wenzel, M J; Bernal, Tapiero J E Building management system with efficient model generation for system identification Patent 2020, (US Non-provisional Patent Number: 10,684,598). @patent{Alanqar202010684598, title = {Building management system with efficient model generation for system identification}, author = {A W I Alanqar and M J Ellis and M J Wenzel and Tapiero J E Bernal}, url = {http://patft1.uspto.gov/netacgi/nph-Parser?patentnumber=10684598}, year = {2020}, date = {2020-01-01}, note = {US Non-provisional Patent Number: 10,684,598}, keywords = {}, pubstate = {published}, tppubtype = {patent} } |
2019 |
Turney, R D; Ellis, M J; Wenzel, M J; ElBsat, M N; Bernal, Tapiero J E; Fentzlaff, B H Smart thermostat with model predictive control Patent 2019, (US Non-provisional Patent Number: 10,495,337). @patent{Turney201910495337, title = {Smart thermostat with model predictive control}, author = {R D Turney and M J Ellis and M J Wenzel and M N ElBsat and Tapiero J E Bernal and B H Fentzlaff}, url = {http://patft1.uspto.gov/netacgi/nph-Parser?patentnumber=10495337}, year = {2019}, date = {2019-11-01}, note = {US Non-provisional Patent Number: 10,495,337}, keywords = {}, pubstate = {published}, tppubtype = {patent} } |
2018 |
Turney, R D; Ellis, M J; Wenzel, M J; ElBsat, M N; Bernal, Tapiero J E; Fentzlaff, B H Smart thermostat with model predictive control Patent 2018, (US Non-provisional Patent Number: 10,146,237). @patent{Turney201810146237, title = {Smart thermostat with model predictive control}, author = {R D Turney and M J Ellis and M J Wenzel and M N ElBsat and Tapiero J E Bernal and B H Fentzlaff}, url = {http://patft1.uspto.gov/netacgi/nph-Parser?patentnumber=10146237}, year = {2018}, date = {2018-06-01}, note = {US Non-provisional Patent Number: 10,146,237}, keywords = {}, pubstate = {published}, tppubtype = {patent} } |
Hofschulz, H A; Turney, R D; Gamroth, T J; Ellis, M J Systems and methods for extending the battery life of a wireless sensor in a building control system Patent 2018, (US Non-provisional Patent Number: 10,042,340). @patent{Hofschulz201810042340, title = {Systems and methods for extending the battery life of a wireless sensor in a building control system}, author = {H A Hofschulz and R D Turney and T J Gamroth and M J Ellis}, url = {http://patft1.uspto.gov/netacgi/nph-Parser?patentnumber=10042340}, year = {2018}, date = {2018-01-01}, note = {US Non-provisional Patent Number: 10,042,340}, keywords = {}, pubstate = {published}, tppubtype = {patent} } |
Master’s Thesis
2021 |
Frasier, A Standard Testing Protocols for HVAC-grade CO2 Sensors and CO2-based Demand Control Ventilation Systems Masters Thesis University of California, Davis, 2021. @mastersthesis{Frasier2021b, title = {Standard Testing Protocols for HVAC-grade CO2 Sensors and CO2-based Demand Control Ventilation Systems}, author = {A. Frasier}, url = {http://ellis.faculty.ucdavis.edu/wp-content/uploads/sites/603/2022/08/A_Frasier_Thesis.pdf}, year = {2021}, date = {2021-08-16}, school = {University of California, Davis}, keywords = {}, pubstate = {published}, tppubtype = {mastersthesis} } |