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

• Published in: Chinese journal of chemical engineering Vol. 17; no. 3; pp. 454 - 459
• Main Author: 张燕 梁秀霞 杨鹏 陈增强 袁著祉
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.06.2009
• Subjects: adaptive inverse control; compound neural network; multi-input multi-output nonlinear system; process control; reaction engineering; 复合神经网络; 多输入多输出; 组成部分; 逆控制器; 递归神经网络; 非线性离散时间系统; 非线性系统; compound neural network; reaction engineering; multi-input multi-output nonlinear system; process control; adaptive inverse control
• ISSN: 1004-9541; 2210-321X
• DOI: 10.1016/S1004-9541(08)60230-X

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.