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

Title: On adaptive trajectory tracking of a robot manipulator using inversion of its neural emulator
Authors: Behera, L
Gopal, M
Chaudhury, S
Keywords: neuro-adaptive trajectory tracking controller
feedforward neural model
robot arm
robot dynamics
extended Kalman filter
Issue Date: 1996
Citation: Neural Networks, IEEE Transactions on, 7(6), 1401 - 1414p.
Abstract: This 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 others
URI: http://eprint.iitd.ac.in/dspace/handle/2074/1862
Appears in Collections:Electrical Engineering

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