Encrypted distributed model predictive control with state estimation for nonlinear processes
This research focuses on encrypted distributed control architectures, aimed at enhancing the operational safety, cybersecurity and computational efficiency of large-scale nonlinear systems, where only partial state measurements are available. In this setup, a distributed model predictive controller...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | Digital Chemical Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772508123000510 |
| _version_ | 1827587808199442432 |
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| author | Yash A. Kadakia Aisha Alnajdi Fahim Abdullah Panagiotis D. Christofides |
| author_facet | Yash A. Kadakia Aisha Alnajdi Fahim Abdullah Panagiotis D. Christofides |
| author_sort | Yash A. Kadakia |
| collection | DOAJ |
| description | This research focuses on encrypted distributed control architectures, aimed at enhancing the operational safety, cybersecurity and computational efficiency of large-scale nonlinear systems, where only partial state measurements are available. In this setup, a distributed model predictive controller (DMPC) is utilized to partition the process into multiple subsystems, each controlled by a distinct Lyapunov-based MPC (LMPC). To consider the interactions among different subsystems, each controller receives and shares with the other controllers control inputs computed for its particular subsystem. As full state feedback is not available, we integrate an extended Luenberger observer with each LMPC, initializing the LMPC model with complete state estimate information provided by the observer. Furthermore, to enhance cybersecurity, wireless signals received and transmitted by the controllers are encrypted. Guidelines are established to implement this proposed control structure in any large-scale nonlinear chemical process network. Simulation results, conducted on a specific nonlinear chemical process network, demonstrate the effective closed-loop performance of the encrypted DMPC with state estimation, utilizing partial state feedback with sensor noise. This is followed by a comprehensive comparison of the closed-loop performance, control input computational time, and suitability of encrypted centralized, decentralized, and distributed MPC frameworks. |
| first_indexed | 2024-03-09T00:24:19Z |
| format | Article |
| id | doaj.art-6cd513b7f5384f53be531fb0eb78de2d |
| institution | Directory Open Access Journal |
| issn | 2772-5081 |
| language | English |
| last_indexed | 2024-03-09T00:24:19Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Digital Chemical Engineering |
| spelling | doaj.art-6cd513b7f5384f53be531fb0eb78de2d2023-12-12T04:37:13ZengElsevierDigital Chemical Engineering2772-50812023-12-019100133Encrypted distributed model predictive control with state estimation for nonlinear processesYash A. Kadakia0Aisha Alnajdi1Fahim Abdullah2Panagiotis D. Christofides3Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, CA, 90095-1592, USADepartment of Electrical and Computer Engineering, University of California, Los Angeles, CA 90095-1592, USADepartment of Chemical and Biomolecular Engineering, University of California, Los Angeles, CA, 90095-1592, USADepartment of Chemical and Biomolecular Engineering, University of California, Los Angeles, CA, 90095-1592, USA; Department of Electrical and Computer Engineering, University of California, Los Angeles, CA 90095-1592, USA; Corresponding author at: Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, CA, 90095-1592, USA.This research focuses on encrypted distributed control architectures, aimed at enhancing the operational safety, cybersecurity and computational efficiency of large-scale nonlinear systems, where only partial state measurements are available. In this setup, a distributed model predictive controller (DMPC) is utilized to partition the process into multiple subsystems, each controlled by a distinct Lyapunov-based MPC (LMPC). To consider the interactions among different subsystems, each controller receives and shares with the other controllers control inputs computed for its particular subsystem. As full state feedback is not available, we integrate an extended Luenberger observer with each LMPC, initializing the LMPC model with complete state estimate information provided by the observer. Furthermore, to enhance cybersecurity, wireless signals received and transmitted by the controllers are encrypted. Guidelines are established to implement this proposed control structure in any large-scale nonlinear chemical process network. Simulation results, conducted on a specific nonlinear chemical process network, demonstrate the effective closed-loop performance of the encrypted DMPC with state estimation, utilizing partial state feedback with sensor noise. This is followed by a comprehensive comparison of the closed-loop performance, control input computational time, and suitability of encrypted centralized, decentralized, and distributed MPC frameworks.http://www.sciencedirect.com/science/article/pii/S2772508123000510Model predictive controlEncrypted controlState estimationDistributed controlCybersecurityProcess control |
| spellingShingle | Yash A. Kadakia Aisha Alnajdi Fahim Abdullah Panagiotis D. Christofides Encrypted distributed model predictive control with state estimation for nonlinear processes Digital Chemical Engineering Model predictive control Encrypted control State estimation Distributed control Cybersecurity Process control |
| title | Encrypted distributed model predictive control with state estimation for nonlinear processes |
| title_full | Encrypted distributed model predictive control with state estimation for nonlinear processes |
| title_fullStr | Encrypted distributed model predictive control with state estimation for nonlinear processes |
| title_full_unstemmed | Encrypted distributed model predictive control with state estimation for nonlinear processes |
| title_short | Encrypted distributed model predictive control with state estimation for nonlinear processes |
| title_sort | encrypted distributed model predictive control with state estimation for nonlinear processes |
| topic | Model predictive control Encrypted control State estimation Distributed control Cybersecurity Process control |
| url | http://www.sciencedirect.com/science/article/pii/S2772508123000510 |
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