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Please use this identifier to cite or link to this item: http://eprint.iitd.ac.in/handle/2074/1862

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dc.contributor.authorBehera, L-
dc.contributor.authorGopal, M-
dc.contributor.authorChaudhury, S-
dc.identifier.citationNeural Networks, IEEE Transactions on, 7(6), 1401 - 1414p.en
dc.description.abstractThis paper is concerned with the design of a neuro-adaptive trajectory tracking controller. The paper presents a new control scheme based on inversion of a feedforward neural model of a robot arm. The proposed control scheme requires two modules. The first module consists of an appropriate feedforward neural model of forward dynamics of the robot arm that continuously accounts for the changes in the robot dynamics. The second module implements an efficient network inversion algorithm that computes the control action by inverting the neural model. In this paper, a new extended Kalman filter (EKF) based network inversion scheme is proposed. The scheme is evaluated through comparison with two other schemes of network inversion: gradient search in input space and Lyapunov function approach. Using these three inversion schemes the proposed controller was implemented for trajectory tracking control of a two-link manipulator. Simulation results in all cases confirm the efficacy of control input prediction using network inversion. Comparison of the inversion algorithms in terms of tracking accuracy showed the superior performance of the EKF based inversion scheme over othersen
dc.format.extent172846 bytes-
dc.subjectneuro-adaptive trajectory tracking controlleren
dc.subjectfeedforward neural modelen
dc.subjectrobot armen
dc.subjectrobot dynamicsen
dc.subjectextended Kalman filteren
dc.titleOn adaptive trajectory tracking of a robot manipulator using inversion of its neural emulatoren
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