Economic Machine-Learning-Based Predictive Control of Nonlinear Systems

Zhe Wu and Panagiotis D. Christofides
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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

Mathematics, 2019, vol. 7, issue 6, 1-20

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.

Keywords: economic model predictive control; recurrent neural networks; ensemble learning; nonlinear systems; parallel computing (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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