Robust Neural Networks Compensating Motion Control of Reconfigurable Manipulator

Author

Li, Ying ; Li, Yuanchun

Author_Institution

Dept. of Control Sci. & Eng., Jilin Univ., Changchun

Volume

2

fYear

2006

fDate

Aug. 30 2006-Sept. 1 2006

Firstpage

388

Lastpage

391

Abstract

There are many uncertainties in real dynamics system of reconfigurable manipulator that makes PID etc. traditional methods control imprecisely. Thus, in this paper, to enhance computed torque control (CTC) based method, robust neural networks (RNN) compensating control scheme is developed to compensate structured and unstructured uncertainties. The controller for a RRP reconfigurable manipulator is designed, uniformly ultimately bounded (UUB) stability is proved by Lyapunov theory and simulations show its effectiveness on satisfactory tracking performance

Keywords

Lyapunov methods; compensation; control system synthesis; manipulator dynamics; motion control; neurocontrollers; robust control; three-term control; torque control; uncertain systems; Lyapunov theory; PID control; RRP reconfigurable manipulator controller design; computed torque control based method; reconfigurable manipulator dynamics system; robust neural network compensating motion control; uniform ultimate bounded stability; Computer networks; Control systems; Manipulator dynamics; Motion control; Neural networks; Recurrent neural networks; Robust control; Three-term control; Torque control; Uncertainty;

fLanguage

English

Publisher

ieee

Conference_Titel

Innovative Computing, Information and Control, 2006. ICICIC '06. First International Conference on

Conference_Location

Beijing

Print_ISBN

0-7695-2616-0

Type

conf

DOI

10.1109/ICICIC.2006.342

Filename

1692007