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

Authors:
Amitava Chatterjee;Keigo Watanabe
Affiliations:
Department of Advanced Systems Control Engineering, Graduate School of Science and Engineering, Saga University, 1 Honjomachi, 840-8502, Saga, Japan;Department of Advanced Systems Control Engineering, Graduate School of Science and Engineering, Saga University, 1 Honjomachi, 840-8502, Saga, Japan
Venue:
Neural Computing and Applications
Year:
2006

Citing 0
Cited 6

Identification using ANFIS with intelligent hybrid stable learning algorithm approaches and stability analysis of training methods

Applied Soft Computing
Training ANFIS as an identifier with intelligent hybrid stable learning algorithm based on particle swarm optimization and extended Kalman filter

Fuzzy Sets and Systems
A Fuzzy Logic Controller tuned with PSO for 2 DOF robot trajectory control

Expert Systems with Applications: An International Journal
A Takagi-Sugeno type neuro-fuzzy network for determining child anemia

Expert Systems with Applications: An International Journal
Adaptive PI Hermite neural control for MIMO uncertain nonlinear systems

Applied Soft Computing
Harmony search-based hybrid stable adaptive fuzzy tracking controllers for vision-based mobile robot navigation

Machine Vision and Applications

Quantified Score

Hi-index	0.00

Visualization

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.