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FAT-Based Robust Adaptive Control of Electrically Driven Robots in Interaction with Environment

Published online by Cambridge University Press:  18 December 2018

Alireza Izadbakhsh*
Affiliation:
Department of Electrical Engineering, Garmsar Branch, Islamic Azad University, Garmsar, Iran E-mail: kheirkhahan_payam@hotmail.com
Payam Kheirkhahan
Affiliation:
Department of Electrical Engineering, Garmsar Branch, Islamic Azad University, Garmsar, Iran E-mail: kheirkhahan_payam@hotmail.com
Saeed Khorashadizadeh
Affiliation:
Faculty of Electrical and Computer Engineering, University of Birjand, 615/97175 Birjand, Iran E-mail: s.khorashadizadeh@birjand.ac.ir
*
*Corresponding author. E-mail: izadbakhsh_alireza@hotmail.com

Summary

This paper presents a robust adaptive impedance controller for robot manipulators using function approximation techniques (FAT). Recently, FAT-based robust impedance controllers have been presented using Fourier series expansion for uncertainty estimation. In fact, sinusoidal functions can approximate nonlinear functions with arbitrary small approximation error based on the orthogonal functions theorem. The novelty of this paper in comparison with previous related works is that the number of required regressor matrices in this paper has been reduced. This superiority becomes more dominant when the manipulator degrees of freedom (DOFs) are increased. First, the desired signals for motor currents are calculated, and then the desired voltages are obtained. In the proposed approach, only a simple model of the actuator and manipulator dynamics is used in the controller design and all the rest dynamics are treated as external disturbance. The external disturbances can then be approximated by Fourier series expansion. The adaptation laws for Fourier series coefficients are derived from a Lyapunov-based stability analysis. Simulation results on a 2-DOF planar robot manipulator including the actuator dynamics indicate the efficiency of proposed method.

Type
Articles
Copyright
Copyright © Cambridge University Press 2018 

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