An optimized Takagi-Sugeno type neuro-fuzzy system for modeling robot manipulators

Amitava Chatterjee, Keigo Watanabe

Research output: Contribution to journalArticle

27 Citations (Scopus)

Abstract

The present paper describes the development of a Takagi-Sugeno (TS)-type Neuro-fuzzy system (NFS) for dynamic modeling of robot manipulators. The NFS has been trained by a relatively new combinatorial metaheuristic optimization method, called particle swarm optimization (PSO). The development of such an intelligent, robust, dynamic models for robot manipulators can immensely help in deriving proper position/velocity control strategies in offline situations with these accurately developed models. The proposed PSO-based NFS has been successfully applied to two-link and three-link model robot manipulators.

Original languageEnglish
Pages (from-to)55-61
Number of pages7
JournalNeural Computing and Applications
Volume15
Issue number1
DOIs
Publication statusPublished - Mar 1 2006
Externally publishedYes

Keywords

  • Neuro-fuzzy systems
  • Particle swarm optimization
  • Robot manipulators

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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