Recurrent fuzzy neural network using genetic algorithm for linear induction motor servo drive

F. J. Lin, P. K. Huang

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

6 Scopus citations

Abstract

A recurrent fuzzy neural network (RFNN) using genetic algorithm (GA) is proposed to control the mover of a linear induction motor (LIM) servo drive for periodic motion in this paper. First, the dynamic model of an indirect field-oriented LIM servo drive is derived. Then, an on-line training RFNN with backpropagation algorithm is introduced as the tracking controller. Moreover, to guarantee the global convergence of tracking error, analytical methods based on a discrete-type Lyapunov function are proposed to determine the varied learning rates of the RFNN. In addition, a real-time GA is developed to search the optimal weights between the membership layer and the rule layer of RFNN on-line. The theoretical analyses for the proposed RFNN using GA controller are described in detail. Finally, experimental results show that the proposed controller provides high-performance dynamic characteristics and is robust with regard to plant parameter variations and external load disturbance.

Original languageEnglish
Title of host publication2006 1st IEEE Conference on Industrial Electronics and Applications
DOIs
StatePublished - 2006
Event2006 1st IEEE Conference on Industrial Electronics and Applications, ICIEA 2006 - Singapore, Singapore
Duration: 24 May 200626 May 2006

Publication series

Name2006 1st IEEE Conference on Industrial Electronics and Applications

Conference

Conference2006 1st IEEE Conference on Industrial Electronics and Applications, ICIEA 2006
Country/TerritorySingapore
CitySingapore
Period24/05/0626/05/06

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