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Model-free adaptive robust control based on TDE for robot with disturbance and input saturation

Published online by Cambridge University Press:  10 August 2023

Xia Liu Open the ORCID record for Xia Liu [Opens in a new window] ,
Lu Wang  and
Yong Yang
Xia Liu*
Affiliation:
School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610039, China
Lu Wang
Affiliation:
School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610039, China
Yong Yang
Affiliation:
School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610039, China
*
Corresponding author: Xia Liu; Email: xliucd@163.com
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Abstract

A model-free adaptive robust control based on time delay estimation (TDE) is proposed for robot in the presence of disturbance and input saturation. TDE is utilized to estimate the complicated nonlinear terms of the robot including unknown dynamics and disturbance, and a TDE error observer is developed to estimate the inevitable TDE error. When the input torque of the robot exceeds the upper or lower limit of the input saturation, an auxiliary system and a saturation deviation boundary adaptive law are employed to mitigate the negative impact of input saturation on the position tracking. Finally, the robust control law is obtained by backstepping. The stability of the closed-loop system is proved by Lyapunov functions, and the validity of the proposed method is demonstrated by comparative simulations and experiments. Compared with the model-based controllers and other model-free controllers, the proposed method does not necessitate the accurate dynamic model of the complicated system and with lower computation. Moreover, it can guarantee the desired position tracking performance of the robot even subject to disturbance and input saturation simultaneously.

Keywords

nonlinear robotunknown dynamics and disturbanceinput saturationtime delay estimationadaptive robust control
Type
Research Article
Information
Robotica , Volume 41 , Issue 11 , November 2023 , pp. 3426 - 3445
Copyright
© The Author(s), 2023. Published by Cambridge University Press

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