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Safeness Index-Based Economic Model Predictive Control of Stochastic Nonlinear Systems

Author

Listed:
  • Zhe Wu

    (Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, CA 90095-1592, USA)

  • Helen Durand

    (Department of Chemical Engineering and Materials Science, Wayne State University, Detroit, MI 48202, USA)

  • Panagiotis D. Christofides

    (Department 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)

Abstract

Process operational safety plays an important role in designing control systems for chemical processes. Motivated by this, in this work, we develop a process Safeness Index-based economic model predictive control system for a broad class of stochastic nonlinear systems with input constraints. A stochastic Lyapunov-based controller is first utilized to characterize a region of the state-space surrounding the origin, starting from which the origin is rendered asymptotically stable in probability. Using this stability region characterization and a process Safeness Index function that characterizes the region in state-space in which it is safe to operate the process, an economic model predictive control system is then developed using Lyapunov-based constraints to ensure economic optimality, as well as process operational safety and closed-loop stability in probability. A chemical process example is used to demonstrate the applicability and effectiveness of the proposed approach.

Suggested Citation

  • Zhe Wu & Helen Durand & Panagiotis D. Christofides, 2018. "Safeness Index-Based Economic Model Predictive Control of Stochastic Nonlinear Systems," Mathematics, MDPI, vol. 6(5), pages 1-19, May.
    • Handle: RePEc:gam:jmathe:v:6:y:2018:i:5:p:69-:d:144275
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    Cited by:

    1. Hanyun Zhou & Wei Li & Jiekai Shi, 2024. "Hierarchically Distributed Charge Control of Plug-In Hybrid Electric Vehicles in a Future Smart Grid," Energies, MDPI, vol. 17(10), pages 1-16, May.

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