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Robust adaptive control of robots using neural network: global tracking stability

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3 Author(s)
C. M. Kwan ; Autom. & Robotics Res. Inst., Texas Univ., Arlington, TX, USA ; D. M. Dawson ; F. L. Lewis

A desired compensation adaptive law-based neural network (DCAL-NN) controller is proposed for the robust position control of rigid-link robots. The NN is used to approximate a highly nonlinear function. The controller can guarantee the global asymptotic stability of tracking errors and boundedness of NN weights. In addition, the NN weights here are tuned on-line, with no off-line learning phase required. When compared with standard adaptive robot controllers, one does not require persistent excitation conditions, linearity in the parameters, or lengthy and tedious preliminary analysis to determine a regression matrix. The controller can be regarded as a universal reusable controller because the same controller can be applied to any type of rigid robots without any modifications

Published in:

Decision and Control, 1995., Proceedings of the 34th IEEE Conference on  (Volume:2 )

Date of Conference:

13-15 Dec 1995