Compensation of unstructured uncertainty in manipulators using neural networks

Author

Kuan, Aaron ; Bavarian, Behnam

Author_Institution

Dept. of Electr. & Comput. Eng., California Univ., Irvine, CA, USA

fYear

1992

fDate

1992

Firstpage

2706

Abstract

A neurocompensator-augmented computed torque control scheme for the compensation of unmodeled frictional effects in manipulators is proposed. The proposed compensator is implemented by a three-layer network structure. A weight adaptation methodology based on the extended Kalman filter algorithm is used. Computer simulations are performed to verify and study the stability, convergence, and trajectory tracking performance of the proposed control architecture. The simulations also verified the stability of the computed torque control law augmented by the neurocompensator approximating unmodeled frictional effects

Keywords

Kalman filters; compensation; filtering and prediction theory; friction; neural nets; robots; stability; convergence; extended Kalman filter; friction compensation; manipulators; neural networks; neurocompensator-augmented computed torque control scheme; stability; trajectory tracking performance; unmodeled frictional effects; unstructured uncertainty; weight adaptation methodology; Artificial neural networks; Backpropagation; Computational modeling; Computer architecture; Computer simulation; Control systems; Convergence; Manipulator dynamics; Nonlinear control systems; Nonlinear dynamical systems; Stability; Torque control; Trajectory; Uncertainty;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 1992., Proceedings of the 31st IEEE Conference on

Conference_Location

Tucson, AZ

Print_ISBN

0-7803-0872-7

Type

conf

DOI

10.1109/CDC.1992.371326

Filename

371326