NDLI: Neural network based adaptive dynamic surface control for flexible-joint robots

Content Provider	IEEE Xplore Digital Library
Author	Liu Jinkun Guo Yi
Copyright Year	2014
Description	Author affiliation: Sch. of Autom. Sci. & Electr. Eng., Beihang Univ., Beijing, China (Liu Jinkun; Guo Yi)
Abstract	A neural network based adaptive dynamic surface controller is proposed for uncertain flexible-joint robot systems. The dynamic surface control method eliminates the problem of “explosion of complexity” existing in traditional backstepping approach by the addition of low pass filters. RBF neural networks are used to approximate the unknown nonlinearities of the model. Nonlinear damping items are used to overcome the external disturbances. Adaptive laws are designed to estimate the weight values of the neural networks and unknown parameters. From Lyapunov stability analysis, it is shown that the control strategy can guarantee the semi-global stability of the closed-loop system and arbitrarily small tracking error by adjusting the controller parameters. Simulation results are presented to validate the good tracking performance of the control system.
Starting Page	8764
Ending Page	8768
File Size	2052797
Page Count	5
File Format	PDF
ISBN	9789881563873
ISSN	19341768
DOI	10.1109/ChiCC.2014.6896473
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-28
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	TCCT, CAA
Subject Keyword	flexible-joint robots dynamic surface control adaptive Three-dimensional displays Backstepping Adaptive systems Neural networks Manipulators Joints neural network
Content Type	Text
Resource Type	Article

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