A sliding mode control using fuzzy-neural hierarchical multi-model identifier

Ieroham Baruch, Jose Luis O. Guzman, Carlos Roman Mariaca-Gaspar, Rosalba Galvan Guerra

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

4 Scopus citations

Abstract

A Recurrent Trainable Neural Network (RTNN) with a two layer canonical architecture learned by a dynamic Backpropagation learning algorithm is incorporated in a Hierarchical Fuzzy-Neural Multi-Model (HFNMM) identifier, combining the fuzzy model flexibility with the learning abilities of the RTNNs. The local and global features of the proposed HFNMM identifier are implemented by a Hierarchical Sliding Mode Controller (HSMC). The proposed HSMC scheme is applied for 1-DOF mechanical plant with friction control, where the obtained comparative results show that the HSMC with a HFNMM identifier outperforms the SMC with a single RTNN identifier.

Original languageEnglish
Title of host publicationTheoretical Advances and Applications of Fuzzy Logic and Soft Computing
EditorsOscar Castillo, Patricia Melin, Oscar Montiel Ross, Roberto Sepulveda Cruz, Witold Pedrycz, Janusz Kacprzyk
Pages762-771
Number of pages10
DOIs
StatePublished - 2007
Externally publishedYes

Publication series

NameAdvances in Soft Computing
Volume42
ISSN (Print)1615-3871
ISSN (Electronic)1860-0794

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