DocumentCode :
294937
Title :
Robust neural network control of flexible-joint robots
Author :
Kwan, C.M. ; Lewis, F.L. ; Kim, Y.H.
Author_Institution :
Autom. & Robotics Res. Inst., Texas Univ., Arlington, TX, USA
Volume :
2
fYear :
1995
fDate :
13-15 Dec 1995
Firstpage :
1296
Abstract :
A robust neural network (NN) controller is proposed for the motion control of rigid-link flexible-joint (RLFJ) robots. No weak elasticity assumption is needed. The NNs are used to approximate three very complicated nonlinear functions. The authors´ NN approach requires no off-line learning phase, no persistent excitation conditions, and no lengthy and tedious preliminary analysis to find a regression matrix. Most importantly, the authors can guarantee the uniformly ultimately bounded (UUB) stability of tracking errors and NN weights. The controller can be regarded as a universal reusable controller because the same controller can be applied to any type of RLFJ robots without any modifications
Keywords :
function approximation; motion control; neurocontrollers; robots; robust control; motion control; nonlinear functions; rigid-link flexible-joint robots; robust neural network control; uniformly ultimately bounded stability; universal reusable controller; Adaptive control; Automatic control; Control systems; Motion control; Neural networks; Orbital robotics; Programmable control; Robot control; Robotics and automation; Robust control;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Decision and Control, 1995., Proceedings of the 34th IEEE Conference on
Conference_Location :
New Orleans, LA
ISSN :
0191-2216
Print_ISBN :
0-7803-2685-7
Type :
conf
DOI :
10.1109/CDC.1995.480276
Filename :
480276
Link To Document :
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