Title :
Robust Neural Networks Compensating Motion Control of Reconfigurable Manipulator
Author :
Li, Ying ; Li, Yuanchun
Author_Institution :
Dept. of Control Sci. & Eng., Jilin Univ., Changchun
fDate :
Aug. 30 2006-Sept. 1 2006
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;
Conference_Titel :
Innovative Computing, Information and Control, 2006. ICICIC '06. First International Conference on
Conference_Location :
Beijing
Print_ISBN :
0-7695-2616-0
DOI :
10.1109/ICICIC.2006.342
