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

张燕; 梁秀霞; 杨鹏; 陈增强; 袁著祉

doi:10.1016/S1004-9541(08)60230-X

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

Updated：2026-01-06

- 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
- 中国化学工程学报（英文版） 2009年17卷第3期页码：454-459
- Affiliations：
  
  1. Department of Automation, Hebei University of Technology,Tianjin,China,300130
  2. Department of Automation, Nankai University,Tianjin,China,300071
- Author bio：
- Funds：
- DOI：10.1016/S1004-9541(08)60230-X
  中图分类号：
- 纸质出版：2009
- Accepted：
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张燕, 梁秀霞, 杨鹏, 等. Modeling and Control of Nonlinear Discrete-time Systems Based on Compound Neural Networks[J]. 中国化学工程学报（英文版）, 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.
张燕, 梁秀霞, 杨鹏, 等. Modeling and Control of Nonlinear Discrete-time Systems Based on Compound Neural Networks[J]. 中国化学工程学报（英文版）, 2009,17(3):454-459. DOI： 10.1016/S1004-9541(08)60230-X.

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. DOI： 10.1016/S1004-9541(08)60230-X.

摘要

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.

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