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 |
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Pages (from-to) | 55-61 |
Number of pages | 7 |
Journal | Neural Computing and Applications |
Volume | 15 |
Issue number | 1 |
DOIs | |
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