Economic Machine-Learning-Based Predictive Control of Nonlinear Systems
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...
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doaj-4244bd08c4854d5c8ebfad8009421f282020-11-25T01:16:17ZengMDPI AGMathematics2227-73902019-06-017649410.3390/math7060494math7060494Economic Machine-Learning-Based Predictive Control of Nonlinear SystemsZhe Wu0Panagiotis D. Christofides1Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, CA 90095-1592, USADepartment of Chemical and Biomolecular Engineering, University of California, Los Angeles, CA 90095-1592, USAIn 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 <i>k</i>-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.https://www.mdpi.com/2227-7390/7/6/494economic model predictive controlrecurrent neural networksensemble learningnonlinear systemsparallel computing |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Zhe Wu Panagiotis D. Christofides |
spellingShingle |
Zhe Wu Panagiotis D. Christofides Economic Machine-Learning-Based Predictive Control of Nonlinear Systems Mathematics economic model predictive control recurrent neural networks ensemble learning nonlinear systems parallel computing |
author_facet |
Zhe Wu Panagiotis D. Christofides |
author_sort |
Zhe Wu |
title |
Economic Machine-Learning-Based Predictive Control of Nonlinear Systems |
title_short |
Economic Machine-Learning-Based Predictive Control of Nonlinear Systems |
title_full |
Economic Machine-Learning-Based Predictive Control of Nonlinear Systems |
title_fullStr |
Economic Machine-Learning-Based Predictive Control of Nonlinear Systems |
title_full_unstemmed |
Economic Machine-Learning-Based Predictive Control of Nonlinear Systems |
title_sort |
economic machine-learning-based predictive control of nonlinear systems |
publisher |
MDPI AG |
series |
Mathematics |
issn |
2227-7390 |
publishDate |
2019-06-01 |
description |
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 <i>k</i>-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. |
topic |
economic model predictive control recurrent neural networks ensemble learning nonlinear systems parallel computing |
url |
https://www.mdpi.com/2227-7390/7/6/494 |
work_keys_str_mv |
AT zhewu economicmachinelearningbasedpredictivecontrolofnonlinearsystems AT panagiotisdchristofides economicmachinelearningbasedpredictivecontrolofnonlinearsystems |
_version_ |
1725150419544440832 |