Simple recurrent neural network-based adaptive predictive control for nonlinear systems

被引：0
|
作者：
Li, Xiang [1 ]
Chen, Zengqiang [1 ]
Yuan, Zhuzhi [1 ]
机构：
[1] Dept. of Automat., Nankai Univ., Tianjin 300071, China
来源：
Asian Journal of Control | 2002年 / 4卷 / 02期
关键词：
Asymptotic stability - Recurrent neural networks;
D O I：
10.1111/j.1934-6093.2002.tb00350.x
中图分类号：
学科分类号：
摘要：
Making use of the neural network universal approximation ability, a nonlinear predictive control scheme is studied in this paper. On the basis of a uniform structure of simple recurrent neural networks, a one-step neural predictive controller (OSNPC) is designed. The whole closed-loop systems asymptotic stability and passivity are discussed, and stable conditions for the learning rate are determined based on the Lyapunov stability theory for the whole neural system. The effectiveness of OSNPC is verified via exhaustive simulations.
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收藏
页码：231 / 239
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