An online training algorithm based on the fusion of sliding mode control theory and fuzzy neural networks with triangular membership functions

Mojtaba Ahmadieh Khanesar*, Erdal Kayacan, Okyay Kaynak, Mohammad Teshnehlab

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

This paper proposes an online tuning method for the parameters of a fuzzy neural network using variable structure systems theory. The proposed learning algorithm establishes a sliding motion in terms of the fuzzy neuro controller parameters, and it leads the error towards zero. The Lyapunov function approach is used to analyze the convergence of the weights for the case of triangular membership functions. Sufficient conditions to guarantee the convergence of the weights are derived. In the simulation studies, the approach presented has been tested on the velocity control of an electro hydraulic servo system in presence of flow nonlinearities and internal friction.

Original languageEnglish
Title of host publicationASCC 2011 - 8th Asian Control Conference - Final Program and Proceedings
Number of pages6
Publication date2011
Pages617-622
Article number5899143
ISBN (Print)9788995605646
Publication statusPublished - 2011
Externally publishedYes
Event8th Asian Control Conference, ASCC 2011 - Kaohsiung, Taiwan, Province of China
Duration: 15 May 201118 May 2011

Conference

Conference8th Asian Control Conference, ASCC 2011
CountryTaiwan, Province of China
CityKaohsiung
Period15/05/201118/05/2011
SponsorMinistry of Education of the People's Republic of China, National Science Council, Bureau of Foreign Trade, National Chiao Tung University

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