Economic Machine-Learning-Based Predictive Control of Nonlinear Systems

Mathematics

卷:7

Zhe Wu¹  PanagiotisD. Christofides¹ 

[1] Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, CA 90095-1592, USA;

关键词: economic model predictive control;    recurrent neural networks;    ensemble learning;    nonlinear systems;    parallel computing;   

DOI  :  10.3390/math7060494

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown