Dynamic neural network for adaptive optimal learning of robot motion with guaranteed convergence rate

Research output: Contribution to journalConference articlepeer-review

Abstract

This paper presents an optimal learning controller for uncertain robot systems which makes use of simple DNN(dynamic neural network) units to estimate uncertain parameters and learn the unknown desired optimal input. With the aid of a Lyapunov function, it is shown that all the error signals in the system are bounded and the robot trajectory converges to the desired one globally exponentially. The effectiveness of the proposed controller is shown by applying the controller to a planar robot manipulator.

Original languageEnglish
Pages (from-to)1315-1319
Number of pages5
JournalProceedings of the IEEE International Conference on Systems, Man and Cybernetics
Volume2
StatePublished - 1996
EventProceedings of the 1996 IEEE International Conference on Systems, Man and Cybernetics. Part 4 (of 4) - Beijing, China
Duration: 14 Oct 199617 Oct 1996

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