Robust adaptive neural network control of uncertain nonholonomic systems with strong nonlinear drifts

被引：42
|
作者：
Wang, ZP [1 ]
Ge, SS [1 ]
Lee, TH [1 ]
机构：
[1] Natl Univ Singapore, Dept Elect & Comp Engn, Singapore 117576, Singapore
来源：
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS | 2004年 / 34卷 / 05期
关键词：
backstepping; neural network (NN); nonholonomic system; robust adaptive control;
D O I：
10.1109/TSMCB.2004.833340
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, robust adaptive neural network (NN) control is presented to solve the control problem of nonholonomic systems in chained form with unknown virtual control coefficients and strong drift nonlinearities. The robust adaptive NN control laws are developed using state scaling and backstepping. Uniform ultimate boundedness of all the signals in the closed-loop are guaranteed, and the system states are proven to converge to a small neighborhood of zero. The control performance of the closed-loop system is guaranteed by appropriately choosing the design parameters. The proposed adaptive NN control is free of control singularity problem. An adaptive control based switching strategy is used to overcome the uncontrollability problem associated with x(0) (t(0)) = 0. The simulation results demonstrate the effectiveness of the proposed controllers.
引用
收藏
页码：2048 / 2059
页数：12
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