An adaptive neural control scheme for mechanical manipulators with guaranteed stability

Author

Barambones, O. ; Etxebarria, V.

Author_Institution

Dept. de Electr. y Electron., Univ. del Pais Vasco, Bilbao, Spain

fYear

1999

fDate

1999

Firstpage

357

Lastpage

362

Abstract

An adaptive neural control scheme for mechanical manipulators is presented. The design basically consists of a neural controller which implements a feedback linearization control law for a generic manipulator with unknown parameters, and a sliding-mode control which improves the robustness of the design and compensates for the neural approximation errors. The resulting closed-loop system is stable and the trajectory-tracking control objective is asymptotically achieved

Keywords

adaptive control; asymptotic stability; closed loop systems; feedback; linearisation techniques; manipulator dynamics; robust control; tracking; variable structure systems; adaptive control; asymptotic stability; closed-loop system; feedback; guaranteed stability; linearization; mechanical manipulators; neurocontrol; sliding-mode control; trajectory-tracking; Adaptive control; Approximation error; Manipulator dynamics; Neural networks; Programmable control; Robots; Robust control; Sliding mode control; Stability; Trajectory;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence in Robotics and Automation, 1999. CIRA '99. Proceedings. 1999 IEEE International Symposium on

Conference_Location

Monterey, CA

Print_ISBN

0-7803-5806-6

Type

conf

DOI

10.1109/CIRA.1999.810074

Filename

810074