A new learning strategy for the two-time-scale neural controller with its application to the tracking control of rigid arms

Author

Cheng, W. ; Wen, J.T.

Author_Institution

NASA Center for Intelligent Robotic Syst. for Space Exploration, Rensselaer Polytech. Inst., Troy, NY, USA

Volume

1

fYear

1992

fDate

7-11 Jun 1992

Firstpage

707

Abstract

A novel fast learning rule with fast weight identification is proposed for the two-time-scale neural controller, and a two-stage learning strategy is developed for the proposed neural controller. The results of the stability analysis show that both the tracking error and the fast weight error will be uniformly bounded and converge to a bounded region which depends only on the accuracy of the slow learning if the system is sufficiently excited. The efficiency of the two-stage learning is also demonstrated by a simulation of a two-link arm

Keywords

learning (artificial intelligence); neural nets; path planning; fast weight identification; learning strategy; rigid arms; simulation; slow learning; stability analysis; tracking control; two-time-scale neural controller; Arm; Control systems; Intelligent robots; NASA; Neural networks; Nonlinear control systems; Nonlinear dynamical systems; Nonlinear systems; Robot control; Stability analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1992. IJCNN., International Joint Conference on

Conference_Location

Baltimore, MD

Print_ISBN

0-7803-0559-0

Type

conf

DOI

10.1109/IJCNN.1992.287104

Filename

287104