A robust neural adaptive force controller for a C5 parallel robot

Author

Achili, B. ; Daachi, B. ; Ali-Cherif, A. ; Amirat, Y.

Author_Institution

Comput. Sci. Lab., Univ. of Paris 8, St. Denis, France

fYear

2009

fDate

22-26 June 2009

Firstpage

1

Lastpage

6

Abstract

In this paper, a neural adaptive force control of a parallel robot is proposed to solve the trajectory tracking problems. Assuming that the dynamic model of the system is a black box one, we use an a priori learning for its neural identification. And then, we use this results to design and adaptive control law. All adaptation laws of neural parameters are based on the stability of the closed loop system in the Lyapunov sense. This approach has been implemented on a C5 parallel robot, and the experimental results show the effectiveness of the proposed method.

Keywords

Lyapunov methods; adaptive control; closed loop systems; force control; neurocontrollers; position control; robots; robust control; C5 parallel robot; Lyapunov stability; a priori learning; adaptation law; closed loop system; neural identification; neural parameter; robust neural adaptive force controller; trajectory tracking; Adaptive control; Force control; Parallel robots; Programmable control; Robust control; Adaptive Control; Artificial Neural Networks; Parallel Robots; Stability;

fLanguage

English

Publisher

ieee

Conference_Titel

Advanced Robotics, 2009. ICAR 2009. International Conference on

Conference_Location

Munich

Print_ISBN

978-1-4244-4855-5

Electronic_ISBN

978-3-8396-0035-1

Type

conf

Filename

5174801