NDLI: Constrained motion control of flexible robot manipulators based on recurrent neural networks

Content Provider	IEEE Xplore Digital Library
Author	Lianfang Tian Jun Wang Zongyuan Mao
Copyright Year	1996
Abstract	In this paper, a neural network approach is presented for the motion control of constrained flexible manipulators, where both the contact force exerted by the flexible manipulator and the position of the end-effector contacting with a surface are controlled. The dynamic equations for vibration of flexible link and constrained force are derived. The developed control scheme can adaptively estimate the underlying dynamics of the manipulator using recurrent neural networks (RNNs). Based on the error dynamics of a feedback controller, a learning rule for updating the connection weights of the adaptive RNN model is obtained. Local stability properties of the control system are discussed. Simulation results are elaborated on for both position and force trajectory tracking tasks in the presence of varying parameters and unknown dynamics, which show that the designed controller performs remarkably well.
Page Count	12
File Size	423966
Starting Page	1541
Ending Page	1552
File Format	PDF
ISSN	10834419
Volume Number	34
Issue Number	3
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-06-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Motion control Robots Recurrent neural networks Manipulator dynamics Force control Adaptive control Neural networks Equations Error correction Programmable control
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Information Systems Electrical and Electronic Engineering Human-Computer Interaction Computer Science Applications Software

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