Neural network model based predictive control for multivariable nonlinear systems

被引：1
|
作者：
Qian, Jixin [1 ]
Yang, Jianfeng [1 ]
Jun, Zhao [1 ]
Jian, Niu [1 ]
机构：
[1] Zhejiang Univ, State Key Lab Ind Control Technol, Hangzhou 310027, Peoples R China
来源：
PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS AND KNOWLEDGE ENGINEERING (ISKE 2007) | 2007年
关键词：
neural networks; model predictive control; nonlinear systems; ARX model;
D O I：
10.2991/iske.2007.101
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A nonlinear model predictive control (NMPC) algorithm based on a BP-ARX combination model is proposed for multivariable nonlinear systems whose static nonlinearity between inputs and outputs could be obtained. The dynamic behavior of the system is described by a parameter varying ARX model, whose parameters are estimated on-line with recursive least-squares algorithm and rescaled properly according to a BP neural network representing the system static nonlinearity. The construction of the BP-ARX model and a constrained NMPC algorithm based on the BP-ARX model are elaborated. The effectiveness of the proposed method is demonstrated by simulation on a multivariable chemical reactor system.
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页数：1
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