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Wavelet neural network-based H∞ trajectory tracking for robot manipulators using fast terminal sliding mode control

Published online by Cambridge University Press:  13 May 2016

Vikas Panwar
Vikas Panwar*
Affiliation:
School of Vocational Studies and Applied Sciences, Gautam Buddha University, Greater Noida - 201310, Uttar Pradesh, India
*
*Corresponding author. E-mail: vikasdma@gmail.com
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This paper focuses on fast terminal sliding mode control (FTSMC) of robot manipulators using wavelet neural networks (WNN) with guaranteed H∞ tracking performance. The FTSMC for trajectory tracking is employed to drive the tracking error of the system to converge to an equilibrium point in finite time. The tracking error arrives at the sliding surface in finite time and then converges to zero in finite time along the sliding surface. To deal with the case of uncertain and unknown robot dynamics, a WNN is proposed to fully compensate the robot dynamics. The online tuning algorithms for the WNN parameters are derived using Lyapunov approach. To attenuate the effect of approximation errors to a prescribed level, H∞ tracking performance is proposed. It is shown that the proposed WNN is able to learn the system dynamics with guaranteed H∞ tracking performance and finite time convergence for trajectory tracking. Finally, the simulation results are performed on a 3D-Microbot manipulator to show the effectiveness of the controller.

Keywords

Robot controlTerminal sliding modeWavelet neural network approximationH∞ tracking performance

Information

Type
Articles
Information
Robotica , Volume 35 , Issue 7 , July 2017 , pp. 1488 - 1503
Copyright
Copyright © Cambridge University Press 2016 

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