LSTM-MPC: A Deep Learning Based Predictive Control Method for Multimode Process Control

Modern industrial processes often operate under different modes, which brings challenges to model predictive control (MPC). Recently, most MPC related methods would establish prediction models independently for different modes, which results in their control effect highly relying on switching strate...

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Bibliographic Details
Published in:IEEE transactions on industrial electronics (1982) Vol. 70; no. 11; pp. 1 - 10
Main Authors: Huang, Keke, Wei, Ke, Li, Fanbiao, Yang, Chunhua, Gui, Weihua
Format: Journal Article
Language:English
Published: New York IEEE 01.11.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptation models
Behavioral sciences
Control methods
Deep learning
Feasibility studies
long short-term memory network
Mathematical models
model predictive control
multimode process
Optimization
Prediction models
Predictive control
Predictive models
Process controls
Stability analysis
Switches
Switching
ISSN:0278-0046, 1557-9948
Online Access:Get full text
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Summary:Modern industrial processes often operate under different modes, which brings challenges to model predictive control (MPC). Recently, most MPC related methods would establish prediction models independently for different modes, which results in their control effect highly relying on switching strategies. Inspired by the powerful representation capabilities of deep learning, this paper proposed a deep learning based MPC method. Specifically, the LSTM network is applied to predict behaviours of controlled system, which can automatically match different operation modes without switching strategy. Then combined with MPC framework, an adaptive gradient descent method is introduced to handle optimization problem and its constraints. In addition, stability and feasibility analysis have been conducted from the aspect of theory to ensure practical application of the proposed method. Experiments on a numerical simulation process and an industrial process platform show the strength and reliability of the proposed method, which reduces the overshoot by about 10<inline-formula><tex-math notation="LaTeX">\%</tex-math></inline-formula> compared to common learning-based MPC methods and improves the control accuracy effectively.
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ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2022.3229323