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

Gongming Wang, Qing Shan Jia, Junfei Qiao, Jing Bi, Mengchu Zhou

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

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.

Original languageEnglish (US)
Article number9189864
Pages (from-to)3643-3652
Number of pages10
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume32
Issue number8
DOIs
StatePublished - Aug 2021

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

Keywords

  • Continuous stirred-tank reactor (CSTR) system
  • growing deep belief network (GDBN) model
  • model predictive control
  • optimal controller
  • transfer learning

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