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 2006
Externally publishedYes

Fingerprint

Fuzzy systems
Manipulators
Robots
Particle swarm optimization (PSO)
Velocity control
Combinatorial optimization
Position control
Dynamic models

Keywords

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

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Artificial Intelligence

Cite this

An optimized Takagi-Sugeno type neuro-fuzzy system for modeling robot manipulators. / Chatterjee, Amitava; Watanabe, Keigo.

In: Neural Computing and Applications, Vol. 15, No. 1, 03.2006, p. 55-61.

Research output: Contribution to journalArticle

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