Adaptive Neural Control for a Class of Uncertain Nonlinear Systems in Pure-Feedback Form With Hysteresis Input

被引：189

作者：

Ren, Beibei ^{[1
]}

Ge, Shuzhi Sam ^{[1
]}

Su, Chun-Yi ^{[2
]}

Lee, Tone Heng ^{[1
]}

机构：

[1] Natl Univ Singapore, Dept Elect & Comp Engn, Singapore 117576, Singapore

[2] Concordia Univ, Dept Mech & Ind Engn, Montreal, PQ H3G 1M8, Canada

来源：

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS | 2009年 / 39卷 / 02期

关键词：

Adaptive control; hysteresis; neural networks (NNs); nonlinear systems; pure-feedback; NETWORK CONTROL;

D O I：

10.1109/TSMCB.2008.2006368

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, adaptive neural control is investigated for a class of unknown nonlinear systems in pure-feedback form with the generalized Prandtl-Ishlinskii hysteresis input. To deal with the nonaffine problem in face of the nonsmooth characteristics of hysteresis, the mean-value theorem is applied successively, first to the functions in the pure-feedback plant, and then to the hysteresis input function. Unknown uncertainties are compensated for using the function approximation capability of neural networks. The unknown virtual control directions are dealt with by Nussbaum functions. By utilizing Lyapunov synthesis, the closed-loop control system is proved to be semiglobally uniformly ultimately bounded, and the tracking error converges to a small neighborhood of zero. Simulation results are provided to illustrate the performance of the proposed approach.

引用

页码：431 / 443

页数：13