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Economic Machine-Learning-Based Predictive Control of Nonlinear Systems

Author

Listed:
  • Zhe Wu

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

In this work, a Lyapunov-based economic model predictive control (LEMPC) method is developed to address economic optimality and closed-loop stability of nonlinear systems using machine learning-based models to make predictions. Specifically, an ensemble of recurrent neural network (RNN) models via a k -fold cross validation is first developed to capture process dynamics in an operating region. Then, the LEMPC using an RNN ensemble is designed to maintain the closed-loop state in a stability region and optimize process economic benefits simultaneously. Parallel computing is employed to improve computational efficiency of real-time implementation of LEMPC with an RNN ensemble. The proposed machine-learning-based LEMPC method is demonstrated using a nonlinear chemical process example.

Suggested Citation

  • Zhe Wu & Panagiotis D. Christofides, 2019. "Economic Machine-Learning-Based Predictive Control of Nonlinear Systems," Mathematics, MDPI, vol. 7(6), pages 1-20, June.
    • Handle: RePEc:gam:jmathe:v:7:y:2019:i:6:p:494-:d:236324
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    Citations

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    Cited by:

    1. Helen Durand, 2020. "Responsive Economic Model Predictive Control for Next-Generation Manufacturing," Mathematics, MDPI, vol. 8(2), pages 1-38, February.
    2. Guilherme V. Hollweg & Shahid A. Khan & Shivam Chaturvedi & Yaoyu Fan & Mengqi Wang & Wencong Su, 2023. "Grid-Connected Converters: A Brief Survey of Topologies, Output Filters, Current Control, and Weak Grids Operation," Energies, MDPI, vol. 16(9), pages 1-31, April.
    3. Giovanni Cicceri & Giuseppe Inserra & Michele Limosani, 2020. "A Machine Learning Approach to Forecast Economic Recessions—An Italian Case Study," Mathematics, MDPI, vol. 8(2), pages 1-20, February.
    4. Zhihao Zhang & Zhe Wu & David Rincon & Panagiotis D. Christofides, 2019. "Real-Time Optimization and Control of Nonlinear Processes Using Machine Learning," Mathematics, MDPI, vol. 7(10), pages 1-25, September.
    5. Monica Aureliana Petcu & Liliana Ionescu-Feleaga & Bogdan-Ștefan Ionescu & Dumitru-Florin Moise, 2023. "A Decade for the Mathematics : Bibliometric Analysis of Mathematical Modeling in Economics, Ecology, and Environment," Mathematics, MDPI, vol. 11(2), pages 1-30, January.
    6. Mohd Shareduwan Mohd Kasihmuddin & Mohd. Asyraf Mansor & Md Faisal Md Basir & Saratha Sathasivam, 2019. "Discrete Mutation Hopfield Neural Network in Propositional Satisfiability," Mathematics, MDPI, vol. 7(11), pages 1-21, November.

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