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 | |
| Publication status | Published - Mar 2006 |
| Externally published | Yes |
Fingerprint
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 journal › Article
}
TY - JOUR
T1 - An optimized Takagi-Sugeno type neuro-fuzzy system for modeling robot manipulators
AU - Chatterjee, Amitava
AU - Watanabe, Keigo
PY - 2006/3
Y1 - 2006/3
N2 - 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.
AB - 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.
KW - Neuro-fuzzy systems
KW - Particle swarm optimization
KW - Robot manipulators
UR - http://www.scopus.com/inward/record.url?scp=28344449332&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=28344449332&partnerID=8YFLogxK
U2 - 10.1007/s00521-005-0008-8
DO - 10.1007/s00521-005-0008-8
M3 - Article
AN - SCOPUS:28344449332
VL - 15
SP - 55
EP - 61
JO - Neural Computing and Applications
JF - Neural Computing and Applications
SN - 0941-0643
IS - 1
ER -