Adaptive neural control for uncertain nonlinear systems in pure-feedback form with hysteresis input

Beibei Ren, Shuzhi Sam Ge, Tong Heng Lee, Chun Yi Su

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations

Abstract

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. The non-affine problem both in the pure-feedback form and in the generalized Prandtl-Ishlinskii hysteresis input function is solved by adopting the Mean Value Theorem. By utilizing Lyapunov synthesis, the closed-loop control system is proved to be semi-globally uniformly ultimately bounded (SGUUB), and the tracking error converges to a small neighborhood of zero. Simulation results are provided to illustrate the performance of the proposed approach.

Original languageEnglish
Title of host publicationProceedings of the 47th IEEE Conference on Decision and Control, CDC 2008
Pages86-91
Number of pages6
DOIs
StatePublished - 2008
Event47th IEEE Conference on Decision and Control, CDC 2008 - Cancun, Mexico
Duration: Dec 9 2008Dec 11 2008

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0191-2216

Conference

Conference47th IEEE Conference on Decision and Control, CDC 2008
CountryMexico
CityCancun
Period12/9/0812/11/08

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