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

Amitava Chatterjee; Keigo Watanabe

doi:10.1007/s00521-005-0008-8

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

Amitava Chatterjee, Keigo Watanabe

Research output: Contribution to journal › Article › peer-review

28 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 language	English
Pages (from-to)	55-61
Number of pages	7
Journal	Neural Computing and Applications
Volume	15
Issue number	1
DOIs	https://doi.org/10.1007/s00521-005-0008-8
Publication status	Published - Mar 1 2006
Externally published	Yes

Keywords

Neuro-fuzzy systems
Particle swarm optimization
Robot manipulators

ASJC Scopus subject areas

Software
Artificial Intelligence

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10.1007/s00521-005-0008-8

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

Abstract

Keywords

ASJC Scopus subject areas

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