Adaptive fuzzy decentralized control of robot manipulators

Author

Sadati, Nasser ; Elhamifar, Ehsan

Author_Institution

Dept. of Electr. Eng., Sharif Univ. of Technol., Tehran

fYear

0

fDate

0-0 0

Lastpage

6

Abstract

In this paper an adaptive fuzzy decentralized control algorithm for trajectory tracking of robot manipulators is developed. The proposed decentralized control algorithm allows the overall closed-loop system to be stabilized while making the tracking error to be uniformly ultimately bounded (UUB), without having any prior knowledge of the robot manipulator dynamics. The interconnections in the dynamic equations of each subsystem are considered with unknown nonlinear bounds. The adaptive fuzzy neural networks (AFNNs) are proposed to model the unknown nonlinear dynamics of the robots and the interconnection terms. Using Lyapunov method, the stability of the overall system is investigated

Keywords

Lyapunov methods; adaptive control; closed loop systems; decentralised control; fuzzy control; manipulator dynamics; neurocontrollers; nonlinear control systems; position control; Lyapunov method; adaptive fuzzy decentralized control; adaptive fuzzy neural networks; closed-loop system; robot manipulator dynamics; trajectory tracking; uniformly ultimately bounded; Adaptive control; Distributed control; Error correction; Fuzzy control; Manipulator dynamics; Nonlinear dynamical systems; Nonlinear equations; Programmable control; Robots; Trajectory;

fLanguage

English

Publisher

ieee

Conference_Titel

Industrial Electronics and Control Applications, 2005. ICIECA 2005. International Conference on

Conference_Location

Quito

Print_ISBN

0-7803-9419-4

Type

conf

DOI

10.1109/ICIECA.2005.1644352

Filename

1644352