Neural-network-based robust adaptive control for a class of nonlinear systems

被引：0
作者：
Chih-Min Lin
Ang-Bung Ting
Ming-Chia Li
Te-Yu Chen
机构：
[1] Yuan Ze University,Department of Electrical Engineering
[2] Chung-Shan Institute of Science and Technology,Information and Communication Research Division
来源：
Neural Computing and Applications | 2011年 / 20卷
关键词：
Neural network; Adaptive control; Uniformly ultimately bounded; Wing rock motion system; Chaotic circuit system;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
In this study, a robust adaptive control (RAC) system is developed for a class of nonlinear systems. The RAC system is comprised of a computation controller and a robust compensator. The computation controller containing a radial basis function (RBF) neural network is the principal controller, and the robust compensator can provide the smooth and chattering-free stability compensation. The RBF neural network is used to approximate the system dynamics, and the adaptive laws are derived to on-line tune the parameters of the neural network so as to achieve favorable estimation performance. From the Lyapunov stability analysis, it is shown that all signals in the closed-loop RBAC system are uniformly ultimately bounded. To investigate the effectiveness of the RAC system, the design methodology is applied to control two nonlinear systems: a wing rock motion system and a Chua’s chaotic circuit system. Simulation results demonstrate that the proposed RAC system can achieve favorable tracking performance with unknown of the system dynamics.
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页码：557 / 563
页数：6
相关论文
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