Publications

@article{Narasimhan2026100279,

title = {Control mode switching for guaranteed detection of false data injection attacks on process control systems},

author = {S. Narasimhan and N. H. El-Farra and M. J. Ellis},

doi = {10.1016/j.dche.2025.100279},

year  = {2026},

date = {2026-01-01},

urldate = {2026-01-01},

journal = {Digital Chemical Engineering},

volume = {18},

pages = {100279},

abstract = {Control-enabled cyberattack detection approaches are necessary for enhancing the cybersecurity of process control systems (PCSs), as evidenced by recent successful cyberattacks against these systems. One type of cyberattack is false data injection attacks (FDIAs), which manipulate data over sensor-controller and/or controller–actuator communication links. This work presents an active detection strategy based on control mode switching, where the control parameters and/or the set-point are adjusted to induce perturbations that reveal stealthy FDIAs which would otherwise go undetected. To guarantee attack detection, the perturbations introduced by the detection method must be “attack-revealing”, a concept formally defined using reachability analysis in this work. Building on this foundation and considering a specific class of FDIAs, a screening algorithm is developed for selecting control modes that guarantee attack-revealing perturbations in the presence of an attack. A theoretical result is established, identifying control modes incapable of guaranteeing attack detection for a subset of these attacks—specifically, non-bias adding attacks, which do not cause a steady-state offset. This result simplifies the screening process by reducing the candidate control mode set and ensuring that only effective control modes are considered. The applicability of the screening algorithm is demonstrated for several FDIAs, including: (1) multiplicative attacks, (2) non-bias adding multiplicative attacks, and (3) replay attacks, where historic process data is injected into communication channels. The simulation results on an illustrative process validate the effectiveness of the modified screening algorithm and the active detection method in detecting non-biased additive and multiplicative replay attacks.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Chakraborty20253536,

title = {Air-to-air multi-function heat pumps for combined space conditioning and domestic water heating},

author = {S. Chakraborty and M. J. Ellis and V. Narayanan},

doi = {10.1016/j.egyr.2025.10.033},

year  = {2025},

date = {2025-11-01},

journal = {Energy Reports},

volume = {14},

pages = {3536--3546},

abstract = {Heat pump adoption in the United States (US) remains slow due to high upfront costs, limited electrical panel capacity, and significant delays associated with electrification. The traditional approach of using separate heat pumps for space conditioning and water heating disproportionately impacts low-income communities and older homes, where costly electrical upgrades and high peak loads from electric resistance heaters present additional barriers. Air-to-Air Multi-Function Heat Pumps (AA-MFHPs) provide space conditioning through direct expansion coils and combines domestic hot water production through an integrated refrigerant circuit. A comprehensive classification of AA-MFHP systems is developed to identify two distinct design types (Refrigerant Reversing Valves and Variable Refrigerant Flow) with their respective advantages and limitations. A comparative assessment of AA-MFHPs with conventional electrification with separate heat pumps in terms of energy, emissions, and costs is conducted by utilizing results from a field test of a single-speed AA-MFHP. It is shown that an AA-MFHP achieves approximately 14.3 % energy savings in summer and 7.5 % in winter compared to separate heat pump systems, with an average installed cost savings of $5000. AA-MFHPs eliminate costly panel upgrades by avoiding electric resistance heating, enabling decarbonization in panel capacity-limited homes while achieving 61 % GHG reductions compared to gas appliances, potentially avoiding 11.35 million metric tons of CO₂-equivalent emissions annually from U.S. single-family homes. Collectively, these benefits position AA-MFHPs as a scalable and cost-effective solution for decarbonizing US homes, highlighting the need for supportive policies, standardized performance metrics, and further research to advance their development and adoption.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Gajjar2025181,

title = {Elucidating the barriers in estimating false data injection attacks in process control systems},

author = {A. Gajjar and N. H. El-Farra and M. J. Ellis},

doi = {10.1016/j.cherd.2025.05.041},

year  = {2025},

date = {2025-06-05},

journal = {Chemical Engineering Research and Design},

volume = {219},

pages = {181--197},

abstract = {Process control systems (PCSs) ensure efficient, safe, and high-quality chemical production. However, their growing reliance on communication networks increases vulnerability to cyberattacks, such as false data injection attacks (FDIAs). FDIAs modify data transferred over the PCS communication links. Addressing FDIAs requires detecting their presence, estimating their parameters, and mitigating their impact. This study examines the identifiability of FDIA parameters—precisely, the ability to estimate these parameters using observable process data. We present cases where attack parameters cannot be uniquely determined from observed data and assess their implications for attack estimation. First, we consider the case when the attack’s impact is indistinguishable from process disturbances or measurement noise. Second, we consider the case when the relationship between the observed data and the attack parameter values is not unique. Both cases result in a lack of identifiability in the attack parameter values from the process data. While the former is tied to inherent process and attack properties, the latter can be addressed using longer observation windows in estimation schemes. We explore these issues through different estimators and demonstrate their impact using a process example. Despite these challenges, we also present scenarios where accurate FDIA estimates can be achieved. Finally, we apply one of the estimators to a chemical process under simultaneous additive and multiplicative FDIAs, showcasing its effectiveness and validating its ability to estimate attack parameters accurately.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{delaRosa2025180,

title = {Practical strategies for managing resistance heating in heat pump water heater predictive control},

author = {L. dela Rosa and C. Mande and M. J. Ellis},

doi = {10.1016/j.cherd.2025.01.024},

year  = {2025},

date = {2025-01-28},

urldate = {2025-01-28},

journal = {Chemical Engineering Research and Design},

volume = {215},

pages = {180--192},

abstract = {As the U.S. grid transitions to 100% carbon-free electricity, adopting electric heat pump water heaters (HPWHs) is key for decarbonizing homes and reducing operating costs for end users. However, their widespread adoption could strain the electric grid. Load-shifting control strategies for HPWHs are needed to shift demand from peak hours to periods with low-cost renewable energy, while ensuring occupant comfort. Economic model predictive control (MPC) can optimize HPWH operation by accounting for physical constraints, tank thermal dynamics, and time-varying factors like electricity prices and hot water demand. A key aspect of the MPC for HPWHs is the use of a thermal energy storage tank model as its prediction model. While various techniques exist for modeling tank thermal stratification, they typically have nonlinear dynamics. Conversely, studies have shown improved performance of HPWHs under MPC with a simplified, low-order tank thermal model compared to the performance under conventional control strategies. This study investigates the adverse effects of enabling resistance heating in MPC with a low-order tank thermal model for heat pump water heaters with two resistance elements, including overheating and unnecessary tank heating. To address these issues, practical strategies, in the form of logic-based constraints, are incorporated into the MPC formulation to manage resistance heating activation and the selection between the two resistance elements. Extensive simulation results are presented to examine the effectiveness of the logic-based constraints in mitigating overheating and unnecessary tank heating in HPWHs under the MPC with a low-order tank thermal model. Additionally, the closed-loop results under the MPC are compared against those under a typical HPWH rule-based control strategy to assess its ability to minimize electricity costs while maintaining occupant comfort.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{delaRosa202583,

title = {Beyond the one-shift wonder: A case study on predictive control for heat pump water heaters},

author = {L. dela Rosa and C. Mande and M. J. Ellis},

doi = {10.1016/j.cherd.2025.01.018},

year  = {2025},

date = {2025-01-23},

journal = {Chemical Engineering Research and Design},

volume = {215},

pages = {83--97},

abstract = {Electric heat pump water heaters (HPWHs) play a crucial role for decarbonizing buildings, but their rapid adoption could strain the grid. Successful decarbonization also requires aligning water heating with periods of clean energy availability. Economic model predictive control (MPC) is ideal for managing HPWH operation as it can account for physical constraints, tank thermal dynamics, and time-varying exogenous inputs (e.g., electricity costs, generation sources of grid energy, and hot water demand). By incorporating time-of-use rates, MPC can shift HPWH loads from high-cost, peak demand hours to low-cost, off-peak periods. However, this can result in a “one-shift wonder”, where MPC pre-heats the HPWH tank prior to the peak period, reducing electricity costs but potentially misaligning with clean energy availability. This work develops a multi-objective MPC that minimizes electricity costs, greenhouse gas (GHG) emissions, and comfort violations for HPWHs. We propose a tuning strategy for the MPC cost function weight to balance these objectives effectively. Specifically, we use the “effective rate” to tune the MPC cost function weight, facilitating the selection of the weight that yields the desired MPC performance. We present two sets of one-day closed-loop simulations of the HPWH under MPC and conventional rule-based control (RBC). In the first set, MPC reduces electricity costs by 41% and GHG emissions by 67% compared to the RBC. The second set examines MPC’s performance under positive and negative correlations between electricity cost and grid GHG emissions, achieving significant reductions in both cost and emissions in each scenario compared to the RBC. Overall, the results demonstrate the effective rate’s utility in tuning the MPC cost function weight. Impact of co-optimization on MPC solution time is also analyzed.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Panicker2025103409,

title = {Terminal set-based cyberattack detection in model predictive control systems with zero false alarms},

author = {R. Panicker and A. Gajjar and N. H. El-Farra and M. J. Ellis},

doi = {10.1016/j.jprocont.2025.103409},

year  = {2025},

date = {2025-01-01},

urldate = {2024-11-13},

journal = {Journal of Process Control},

volume = {149},

pages = {103409},

abstract = {The increased reliance of industrial control systems on networked components has made them more vulnerable to cyberattacks, necessitating cyberattack detection schemes specifically designed for detecting cyberattacks affecting industrial control systems. This work presents a set-membership-based detection scheme for systems under model predictive control (MPC). Specifically, we consider steady-state operation because many systems operate over long periods near a desired steady state. Provided the disturbances and measurement noise acting on the system are sufficiently small, we show that the closed-loop system under MPC is equivalent to the closed-loop system under a linear quadratic regulator, formulated with the same stage cost and weighting matrices, in a region containing the desired operating point. This equivalence is leveraged to show that the minimum robust positively invariant (mRPI) sets under both controllers are equivalent, enabling the calculation of the mRPI set for the closed-loop system under MPC. Using the mRPI set of the attack-free system, we present an attack detection scheme for systems under MPC and derive conditions under which the attack detection scheme applied to the attack-free closed-loop system does not raise an alarm. The detection scheme is applied to a simplified (linear) building space-cooling system to demonstrate that it does not raise false alarms during attack-free operation and that it successfully detects attacks when the system is subjected to a multiplicative false-data injection attack altering the data communicated over the sensor-controller link. Furthermore, the detection scheme’s applicability to nonlinear systems is assessed. Specifically, the detection scheme is applied to a nonlinear chemical process to demonstrate that the detection scheme does not raise false alarms during attack-free operation and successfully detects an attack when the process is subjected to a false-data injection cyberattack.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Marr202402524002,

title = {Toward clean and green buildings},

author = {L. C. Marr and C. D. Cappa and W. P. Bahnfleth and T. H. Bertram and R. L. Corsi and M. J. Ellis and G. P. Henze and G. Isaacman-Vanwertz and S. L. Miller and T. Pistochini and W. D. Ristenpart and M. E. Vance and P. J. Vikesland},

doi = {10.1061/JOEEDU.EEENG-7727},

year  = {2024},

date = {2024-06-27},

journal = {Journal of Environmental Engineering},

volume = {150},

pages = {02524002},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Narasimhan2024327,

title = {Detection of multiplicative false data injection cyberattacks on process control systems via randomized control mode switching},

author = {S. Narasimhan and M. J. Ellis and N. H. El-Farra},

doi = {10.3390/pr12020327},

year  = {2024},

date = {2024-02-02},

journal = {Processes},

volume = {12},

pages = {327},

abstract = {A fundamental problem at the intersection of process control and operations is the design of detection schemes monitoring a process for cyberattacks using operational data. Multiplicative false data injection (FDI) attacks modify operational data with a multiplicative factor and could be designed to be detection evading without in-depth process knowledge. In a prior work, we presented a control mode switching strategy that enhances the detection of multiplicative FDI attacks in processes operating at steady state (when process states evolve within a small neighborhood of the steady state). Control mode switching on the attack-free process at steady-state may induce transients and generate false alarms in the detection scheme. To minimize false alarms, we subsequently developed a control mode switch-scheduling condition for processes with an invertible output matrix. In the current work, we utilize a reachable set-based detection scheme and use randomized control mode switches to augment attack detection capabilities. The detection scheme eliminates potential false alarms occurring from control mode switching, even for processes with a non-invertible output matrix, while the randomized switching helps bolster the confidentiality of the switching schedule, preventing the design of a detection-evading “smart” attack. We present two simulation examples to illustrate attack detection without false alarms, and the merits of randomized switching (compared with scheduled switching) for the detection of a smart attack.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Pistochini2023113717,

title = {Method of test for CO2 -based demand control ventilation systems: Benchmarking the state-of-the-art and the undervalued potential of proportional-integral control},

author = {T. E. Pistochini and M. J. Ellis and F. Meyers and A. Frasier and C. Cappa and D. Bennett},

doi = {10.1016/j.enbuild.2023.113717},

year  = {2023},

date = {2023-11-02},

urldate = {2023-11-02},

journal = {Energy & Buildings},

volume = {301},

pages = {113717},

abstract = {Carbon dioxide (CO2) based demand control ventilation (DCV) adjusts a building’s outdoor air ventilation rate in response to indoor CO2 concentration to save energy while maintaining indoor air quality. Packaged heating, ventilation, and air-conditioning systems often contain DCV controllers with embedded proprietary algorithms that lack transparent performance data. A test method was developed to assess the ability of a DCV controller to maintain the indoor CO2 concentration at a setpoint in response to a series of CO2 generation functions that represent three different building occupancy densities and two occupancy schedules. Six commercially available controllers were tested to demonstrate the method and provide directly comparable results. The performance (in terms of CO2 control and damper movement) of each controller tested was compared to the performance of an ideal controller which knows the CO2 generation function. Finally, the performance of a proportional-integral (PI) controller with preset gains was developed and tested to determine the potential maximum performance achievable with this control strategy. The best performing commercially available controller achieved CO2 control (within 75 ppm of the setpoint) approximately 80 % of the time with damper movement slightly less than an ideal controller. However, most of the commercially available controllers had marginal or poor performance for CO2 control and damper movement. Two controllers had damper movement more than three times the ideal controller. Notably, a PI algorithm configured and tested by the research team achieved superior performance with CO2 control 92 % of the time and damper movement 1.5 times the ideal controller.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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%.},

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}

@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.},

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}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}