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Self-organizing neural networks for learning inverse dynamics of robot manipulator
Laxmidhar Behera, , Santanu Chaudhury
Published in IEEE, Piscataway, NJ, United States
Pages: 457 - 460
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, self-organizing 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.
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Published in IEEE, Piscataway, NJ, United States
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