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FAT-based robust adaptive controller design for electrically direct-driven robots using Phillips q-Bernstein operators

Published online by Cambridge University Press:  15 March 2022

Alireza Izadbakhsh Open the ORCID record for Alireza Izadbakhsh [Opens in a new window] ,
Ali Akbarzadeh Kalat  and
Nazila Nikdel
Alireza Izadbakhsh*
Affiliation:
Department of Electrical Engineering, Garmsar Branch, Islamic Azad University, Garmsar, Iran
Ali Akbarzadeh Kalat
Affiliation:
Faculty of Electrical Engineering, Shahrood University of Technology, Shahrood, Iran
Nazila Nikdel
Affiliation:
Faculty of Electrical and Computer Engineering, Urmia University, Urmia, Iran
*
*Corresponding author. E-mail: [email protected]
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Abstract

This article proposes a robust and adaptive controller for industrial robot arms with multiple degrees of freedom without the need for velocity measurement. Many of the controllers designed for manipulators are model-based and require detailed knowledge of the system model. In contrast to these methods, this paper proposes a model-free controller using the Philips q-Bernstein operator as universal approximator. The designed controller can approximate uncertainties including external disturbances and unmodeled dynamics based on its universal approximation capability. Besides, most of the controllers revealed for robot arms are torque-based, which is not a realistic presumption from a practical point of view. In the proposed control method, the voltage applied to the actuator is considered as the control signal. However, unlike many voltage-based methods, the need to know the exact models of the system and the actuator has been eliminated in the presented method. Also, adaptive rules are extracted during the Lyapunov analysis to ensure system stability. Finally, to analyze the performance of the presented controller, this method is simulated for an industrial robot arm, and the results are analyzed. The proposed methodology is also compared to those of a strong state-of-the-art approximator, the Chebyshev neural network.

Keywords

function approximation techniqueindustrial manipulatoruncertaintiesPhilips q-Bernstein operatoradaptive controlChebyshev neural network
Type
Research Article
Information
Robotica , Volume 40 , Issue 10 , October 2022 , pp. 3415 - 3434
Copyright
© The Author(s), 2022. Published by Cambridge University Press

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