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Please use this identifier to cite or link to this item: http://eprint.iitd.ac.in/handle/2074/1741

Title: Self-organizing neural networks for learning inverse dynamics of robot manipulator
Authors: Behera, L
Gopal, M
Chaudhury, S
Keywords: self-organizing map
Kohonen's self-organizing topology conserving feature map
neural-gas algorithm
robot control
Issue Date: 1995
Citation: Industrial Automation and Control, IEEE/IAS International Conference on, 457 - 460p.
Abstract: Fast and accurate trajectory tracking of a robot arm primarily depends on the knowledge of its explicit inverse dynamics model. Online learning of inverse dynamics using a supervised learning algorithm is difficult in the absence of a priori knowledge of command error. On the other hand, a self-organizing neural network employing an unsupervised learning scheme does not depend on the command error. These networks are suitable for both off-line and online schemes of learning the inverse dynamics. The present paper proposes two schemes based on 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
URI: http://eprint.iitd.ac.in/dspace/handle/2074/1741
Appears in Collections:Electrical Engineering

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