Dynamically Weighted Model Predictive Control of Affine Nonlinear Systems Based on Two-Timescale Neurodynamic Optimization

Jiasen Wang, Jun Wang, Dongbin Zhao

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper discusses dynamically weighted model predictive control based on two-timescale neurodynamic optimization. Minimax optimization problems with dynamic weights in objective functions are used in the model predictive control. The minimax optimization problems are solved by using a two-timescale neurodynamic optimization approach. Examples on controlling HVAC (heating, ventilation, and air-conditioning) and CSTR (cooling continuous stirred tank reactor) systems are elaborated to substantiate the efficacy of the control approach.

Original languageEnglish
Title of host publicationAdvances in Neural Networks – ISNN 2020 - 17th International Symposium on Neural Networks, ISNN 2020, Proceedings
EditorsMin Han, Sitian Qin, Nian Zhang
PublisherSpringer Science and Business Media Deutschland GmbH
Pages96-105
Number of pages10
ISBN (Print)9783030642204
DOIs
Publication statusPublished - 2020
Externally publishedYes
Event17th International Symposium on Neural Networks, ISNN 2020 - Cairo, Egypt
Duration: 4 Dec 20206 Dec 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12557 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference17th International Symposium on Neural Networks, ISNN 2020
Country/TerritoryEgypt
CityCairo
Period4/12/206/12/20

Keywords

  • CSTR
  • HVAC
  • Model predictive control
  • Neurodynamic optimization

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