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SELF-ORGANIZING NEURAL NETWORKS FOR LEARNING INVERSE DYNAMICS OF ROBOT MANIPULATOR

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Abstract

Fast and accurate trajectory tracking of a robot arm primarily depends on the knowledge of its ' explicit inverse dynamics model. On line learning of inverse dynamics using supervised learning algorithm is difficult in the absence of a priori knowledge of command error. On the other hand, selforganizing neural network employing unsupervised learning scheme does not depend on the command error. These networks are suitable for both off-line and on-line schemes of learning the inverse dynamics. The present paper proposes both the schemes based on two unsupervised learning algorithms, namely, Kohonen's self-organizing topology conserving feature map and "neural gas " algorithm. Simulation results on a single link manipulator confirms the efficacy of the proposed schemes. Key Words: Self-organizing map, Kohonen 's topology conserving feature map, Neural gas algorithm. Robot control

Year: 2009
OAI identifier: oai:CiteSeerX.psu:10.1.1.134.7399
Provided by: CiteSeerX
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