Deep Learning-Based Model Predictive Control for Continuous Stirred-Tank Reactor System

Document Type

Article

Publication Date

8-1-2021

Abstract

A continuous stirred-tank reactor (CSTR) system is widely applied in wastewater treatment processes. Its control is a challenging industrial-process-control problem due to great difficulty to achieve accurate system identification. This work proposes a deep learning-based model predictive control (DeepMPC) to model and control the CSTR system. The proposed DeepMPC consists of a growing deep belief network (GDBN) and an optimal controller. First, GDBN can automatically determine its size with transfer learning to achieve high performance in system identification, and it serves just as a predictive model of a controlled system. The model can accurately approximate the dynamics of the controlled system with a uniformly ultimately bounded error. Second, quadratic optimization is conducted to obtain an optimal controller. This work analyzes the convergence and stability of DeepMPC. Finally, the DeepMPC is used to model and control a second-order CSTR system. In the experiments, DeepMPC shows a better performance in modeling, tracking, and antidisturbance than the other state-of-the-art methods.

Identifier

85112002681 (Scopus)

Publication Title

IEEE Transactions on Neural Networks and Learning Systems

External Full Text Location

https://doi.org/10.1109/TNNLS.2020.3015869

e-ISSN

21622388

ISSN

2162237X

PubMed ID

32903185

First Page

3643

Last Page

3652

Issue

8

Volume

32

Grant

61703011

Fund Ref

National Natural Science Foundation of China

This document is currently not available here.

Share

COinS