NDLI: Adaptive neural learning control of rigid-link electrically-driven robot manipulators

Content Provider	IEEE Xplore Digital Library
Author	Wu Yuxiang He Qizhen Wang Cong
Copyright Year	2011
Description	Author affiliation: South China University of Technology, Guangzhou 510641, China (Wu Yuxiang; He Qizhen; Wang Cong)
Abstract	Based on deterministic learning theory which states that an appropriately designed adaptive neural controller can learn the unknown system internal dynamics during a stable control process, this paper investigates deterministic learning from adaptive neural control of rigid-link electrically-driven (RLED) robot manipulators with completely unknown system dynamics. Firstly, the recent results on localized RBF networks and stability analysis of linear time-varying (LTV) systems are presented. Secondly, a stable adaptive neural control algorithm is designed for RLED robot manipulators, and the closed-loop control system with the LTV form is obtained. Deterministic learning of RLED robot manipulators is analyzed, locally-accurate approximation of the closed-loop control system dynamics is achieved along the periodic tracking orbit. Improved control performance is achieved using learned knowledge stored as a set of constant neural weights. Finally, simulation example is presented to demonstrate the effectiveness of the proposed control algorithm
Starting Page	6616
Ending Page	6622
File Size	239587
Page Count	7
File Format	PDF
ISBN	9781457706776
ISSN	21612927
e-ISBN	9789881725592
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-22
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Chinese Assoc of Automati
Subject Keyword	Adaptive systems Adaptive Control Artificial neural networks Radial basis function networks Deterministic Learning Orbits RBF Networks Electrically-driven Robot Manipulators Manipulator dynamics
Content Type	Text
Resource Type	Article

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