Encrypted distributed model predictive control with state estimation for nonlinear processes

Yash A. Kadakia, Aisha Alnajdi, Fahim Abdullah, Panagiotis D. Christofides

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish
Article number100133
JournalDigital Chemical Engineering
Volume9
DOIs
StatePublished - Dec 2023
Externally publishedYes

Keywords

  • Cybersecurity
  • Distributed control
  • Encrypted control
  • Model predictive control
  • Process control
  • State estimation

Fingerprint

Dive into the research topics of 'Encrypted distributed model predictive control with state estimation for nonlinear processes'. Together they form a unique fingerprint.

Cite this