Recurrent neuro fuzzy control design for tracking of mobile robots via hybrid algorithm

Ching Hung Lee*, Ming Hui Chiu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

30 Scopus citations

Abstract

This paper proposes a TSK-type recurrent neuro fuzzy system (TRNFS) and hybrid algorithm- GA_BPPSO to develop a direct adaptive control scheme for stable path tracking of mobile robots. The TRNFS is a modified model of the recurrent fuzzy neural network (RFNN) to obtain generalization and fast convergence. The TRNFS is designed using hybridization of genetic algorithm (GA), back-propagation (BP), and particle swarm optimization (PSO), called GA_BPPSO. For the tracking control of mobile robot, two TRNFSs are designed to generate the control inputs by direct adaptive control scheme and hybrid algorithm GA_BPPSO. Through simulation results, we demonstrate the effectiveness of our proposed controller.

Original languageEnglish
Pages (from-to)8993-8999
Number of pages7
JournalExpert Systems with Applications
Volume36
Issue number5
DOIs
StatePublished - Jul 2009

Keywords

  • Adaptive
  • Fuzzy neural system
  • Learning
  • Nonlinear control
  • Recurrent

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