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

被引：13

作者：

Lin, Chih-Min ^{[1
]}

Ting, Ang-Bung ^{[1
]}

Li, Ming-Chia ^{[1
]}

Chen, Te-Yu ^{[2
]}

机构：

[1] Yuan Ze Univ, Dept Elect Engn, Tao Yuan 320, Taiwan

[2] Chung Shan Inst Sci & Technol, Informat & Commun Res Div, Tao Yuan 325, Taiwan

来源：

NEURAL COMPUTING & APPLICATIONS | 2011年 / 20卷 / 04期

关键词：

Neural network; Adaptive control; Uniformly ultimately bounded; Wing rock motion system; Chaotic circuit system; SLENDER DELTA-WINGS; TRACKING CONTROL; ROCK;

D O I：

10.1007/s00521-011-0561-2

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

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.

引用

页码：557 / 563

页数：7

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