Model predictive control of nonlinear affine systems based on the general projection neural network and its application to a continuous stirred tank reactor

Zheng Yan, Jun Wang

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

8 Citations (Scopus)

Abstract

Model predictive control (MPC) is an advanced technique for process control. It is based on iterative, finite horizon optimization of a cost function associated with a plant model. Neural network is an effective approach for on-line optimization problems. In this paper, we apply the general projection neural network for MPC of nonlinear affine systems. Continuous stirred tank reactor (CSTR) system is a typical chemical reactor widely used in chemical industry and can be characterized as a nonlinear affine system. The general projection neural network based MPC is applied to the CSTR problem with input and output constraints. This application demonstrates the usefulness and effectiveness of proposed MPC approach to industrial problems.

Original languageEnglish
Title of host publication2011 International Conference on Information Science and Technology, ICIST 2011
Pages1011-1015
Number of pages5
DOIs
Publication statusPublished - 2011
Externally publishedYes
Event2011 International Conference on Information Science and Technology, ICIST 2011 - Nanjing, China
Duration: 26 Mar 201128 Mar 2011

Publication series

Name2011 International Conference on Information Science and Technology, ICIST 2011

Conference

Conference2011 International Conference on Information Science and Technology, ICIST 2011
Country/TerritoryChina
CityNanjing
Period26/03/1128/03/11

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