Modeling and Control of Nonlinear Discrete-time Systems Based on Compound Neural Networks

›› 2009, Vol. 17 ›› Issue (3): 454-459.

Modeling and Control of Nonlinear Discrete-time Systems Based on Compound Neural Networks

张燕¹, 梁秀霞¹, 杨鹏¹, 陈增强², 袁著祉²

1. Department of Automation, Hebei University of Technology, Tianjin 300130, China;
2. Department of Automation, Nankai University, Tianjin 300071, China

收稿日期:2008-06-24 修回日期:2009-03-20 出版日期:2009-06-28 发布日期:2009-06-28
通讯作者: ZHANG Yan,E-mail:yzhangzz@yahoo.com.cn
基金资助:
Supported by the National Natural Science Foundation of China (60575009, 60574036)

Modeling and Control of Nonlinear Discrete-time Systems Based on Compound Neural Networks

ZHANG Yan¹, LIANG Xiuxia¹, YANG Peng¹, CHEN Zengqiang², YUAN Zhuzhi²

1. Department of Automation, Hebei University of Technology, Tianjin 300130, China;
2. Department of Automation, Nankai University, Tianjin 300071, China

Received:2008-06-24 Revised:2009-03-20 Online:2009-06-28 Published:2009-06-28
Supported by:
Supported by the National Natural Science Foundation of China (60575009, 60574036)

摘要/Abstract

摘要： An adaptive inverse controller for nonliear discrete-time system is proposed in this paper. A compound neural network is constructed to identify the nonlinear system, which includes a linear part to approximate the nonlinear system and a recurrent neural network to minimize the difference between the linear model and the real nonlinear system. Because the current control input is not included in the input vector of recurrent neural network (RNN), the inverse control law can be calculated directly. This scheme can be used in real-time nonlinear single-input single-output (SISO) and multi-input multi-output (MIMO) system control with less computation work. Simulation studies have shown that this scheme is simple and affects good control accuracy and robustness.

关键词: adaptive inverse control, compound neural network, process control, reaction engineering, multi-input multi-output nonlinear system

Abstract: An adaptive inverse controller for nonliear discrete-time system is proposed in this paper. A compound neural network is constructed to identify the nonlinear system, which includes a linear part to approximate the nonlinear system and a recurrent neural network to minimize the difference between the linear model and the real nonlinear system. Because the current control input is not included in the input vector of recurrent neural network (RNN), the inverse control law can be calculated directly. This scheme can be used in real-time nonlinear single-input single-output (SISO) and multi-input multi-output (MIMO) system control with less computation work. Simulation studies have shown that this scheme is simple and affects good control accuracy and robustness.

Key words: adaptive inverse control, compound neural network, process control, reaction engineering, multi-input multi-output nonlinear system

张燕, 梁秀霞, 杨鹏, 陈增强, 袁著祉. Modeling and Control of Nonlinear Discrete-time Systems Based on Compound Neural Networks[J]. , 2009, 17(3): 454-459.

ZHANG Yan, LIANG Xiuxia, YANG Peng, CHEN Zengqiang, YUAN Zhuzhi. Modeling and Control of Nonlinear Discrete-time Systems Based on Compound Neural Networks[J]. , 2009, 17(3): 454-459.

参考文献

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Modeling and Control of Nonlinear Discrete-time Systems Based on Compound Neural Networks

Modeling and Control of Nonlinear Discrete-time Systems Based on Compound Neural Networks

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