DocumentCode :
1981690
Title :
Output feedback control of rigid robots using dynamic neural networks
Author :
Kim, Young H. ; Lewis, Frank L.
Author_Institution :
Inst. of Autom. & Robotics Res., Texas Univ., Arlington, TX, USA
Volume :
2
fYear :
1996
fDate :
22-28 Apr 1996
Firstpage :
1923
Abstract :
A robust neural network (NN) output feedback scheme is proposed for the motion control of rigid robots. A dynamic NN observer is presented to estimate the joint speeds. The stability of a closed-loop system composed of a robot, a NN observer, and a NN controller is proven. The NN weights in both the observer and the controller are tuned online, with no off-line learning phase required. Most importantly, we can guarantee the boundness of the estimated velocities, the position tracking errors, and the NN weights. Also no exact knowledge of the robot dynamics is required so that the NN controller is model-free and so applicable to any type of rigid robot. When compared with adaptive-type controllers, we do not require persistent excitation conditions, linearity in the unknown system parameters, or the tedious computation of a regression matrix. Thus the new NN approach represents an improvement over adaptive techniques
Keywords :
closed loop systems; feedback; neurocontrollers; robots; robust control; adaptive-type controllers; closed-loop system; dynamic neural networks; dynamic observer; joint speed estimation; motion control; output feedback control; position tracking errors; rigid robots; robust neural network output feedback scheme; stability; Adaptive control; Control systems; Motion control; Neural networks; Nonlinear dynamical systems; Output feedback; Position measurement; Robot control; Robotics and automation; Velocity control;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Robotics and Automation, 1996. Proceedings., 1996 IEEE International Conference on
Conference_Location :
Minneapolis, MN
ISSN :
1050-4729
Print_ISBN :
0-7803-2988-0
Type :
conf
DOI :
10.1109/ROBOT.1996.506992
Filename :
506992
Link To Document :
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