Adaptive sliding mode control using RBF neural network for nonlinear system

Ming Guang Zhang, Yu Wu Chen, Peng Wang, Zhao Gang Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

A novel adaptive sliding mode controller based on Radial Basis Function neural network (RBFNN) is proposed in this paper for the nonlinear systems with uncertainties using feedback linearization method. An adaptive rule is utilized to on-line adjusting the weights of RBFNN, which is used to compute the equivalent control. Adaptive training algorithm was derived in the sense of Lyapunov stability analysis, so that the stability of the closed-loop system can be guaranteed even in the case of uncertainty. Using the RBFNN, instead of multilayer feed forward network trained with back propagation, works out shorter reaching time. Chattering problem of SMC is substantially derived in the proposed controller. Simulation Jesuits show that the position hacking responses closely follow the desired trajectory occurrence of the disturbances. Also, simulation results demonstrate that the proposed controller is a stable control scheme for the inverted pendulum trajectory hacking applications and has strong rubu stness.

Original languageEnglish
Title of host publicationProceedings of the 7th International Conference on Machine Learning and Cybernetics, ICMLC
Pages1860-1865
Number of pages6
DOIs
StatePublished - 2008
Externally publishedYes
Event7th International Conference on Machine Learning and Cybernetics, ICMLC - Kunming, China
Duration: 12 Jul 200815 Jul 2008

Publication series

NameProceedings of the 7th International Conference on Machine Learning and Cybernetics, ICMLC
Volume4

Conference

Conference7th International Conference on Machine Learning and Cybernetics, ICMLC
Country/TerritoryChina
CityKunming
Period12/07/0815/07/08

Keywords

  • Adaptive
  • Feedback linearization
  • Inverted pendulum
  • RBF neural network
  • Sliding mode control

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