| 基于复合神经网络的离散非线性系统建模与控制 |
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| 引用本文： | 张燕,梁秀霞,杨鹏,陈增强,袁著祉.基于复合神经网络的离散非线性系统建模与控制[J].中国化学工程学报,2009,17(3):454-459. |
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| 作者姓名： | 张燕 梁秀霞 杨鹏 陈增强 袁著祉 |
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| 作者单位： | 1. Department of Automation, Hebei University of Technology, Tianjin 300130, China;2. Department of Automation, Nankai University, Tianjin 300071, China |
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| 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.
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| 关 键 词： | adaptive inverse control compound neural network process control reaction engineering multi-input multi-output nonlinear system |
| 收稿时间： | 24 June 2008 |
| 修稿时间： | 2008-6-24
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Modeling and Control of Nonlinear Discrete-time Systems Based on Compound Neural Networks |
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| ZHANG Yan,LIANG Xiuxia,YANG Peng,CHEN Zengqiang,YUAN Zhuzhi.Modeling and Control of Nonlinear Discrete-time Systems Based on Compound Neural Networks[J].Chinese Journal of Chemical Engineering,2009,17(3):454-459. |
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| Authors: | ZHANG Yan LIANG Xiuxia YANG Peng CHEN Zengqiang YUAN Zhuzhi |
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| Affiliation: | 1. Department of Automation, Hebei University of Technology, Tianjin 300130, China;2. Department of Automation, Nankai University, Tianjin 300071, China |
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| 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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| Keywords: | adaptive inverse control compound neural network process control reaction engineering multi-input multi-output nonlinear system |
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