Neurocontroller via adaptive learning rates for stable path tracking of mobile robots

Sung Jin Yoo, Jin Bae Park, Yoon Ho Choi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we present a neurocontroller via adaptive learning rates (ALRs) for stable path tracking of mobile robots. The self recurrent wavelet neural networks (SRWNNs) are employed as two neurocontrollers for the control of the mobile robot. Since the SRWNN combines the advantages such as the multi-resolution of the wavelet neural network and the information storage of the recurrent neural network, it can easily cope with the unexpected change of the system. Specially, the ALR algorithm in the gradient-descent method is extended for the multi-input multi-output system and is applied to train the parameters of the SRWNN controllers. The ALRs are derived from the discrete Lyapunov stability theorem, which are used to guarantee the stable path tracking of mobile robots. Finally, through computer simulations, we demonstrate the effectiveness and stability of the proposed controller.

Original languageEnglish
Title of host publicationAdvances in Natural Computation - Second International Conference, ICNC 2006, Proceedings,
PublisherSpringer Verlag
Pages408-417
Number of pages10
ISBN (Print)3540459014, 9783540459019
DOIs
Publication statusPublished - 2006
Event2nd International Conference on Natural Computation, ICNC 2006 - Xi'an, China
Duration: 2006 Sept 242006 Sept 28

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4221 LNCS - I
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other2nd International Conference on Natural Computation, ICNC 2006
Country/TerritoryChina
CityXi'an
Period06/9/2406/9/28

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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