Robust iterative learning control for rehabilitation robot in the presence of parametric uncertainty

Subject Areas : Journal of New Applied and Computational Findings in Mechanical Systems

Mojtaba Ayatinia ¹ , mehdi Forouzanfar ² , Amin Ramezani ³

1 - Department of Electrical Engineering, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran
2 - Department of Electrical Engineering, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran
3 - Electrical and Computer Engineering Department, Tarbiat Modarres University, Tehran, Iran

Received: 2022-02-12 Accepted : 2022-03-16 Published : 2022-02-20

Keywords: Parametric Uncertainty, Iterative Learning Control (ILC), Rehabilitation Robot, Fixed Learning Gain,

Abstract :

In this paper, the robust convergence of iterative learning control (ILC) in a linear rehabilitation robot with parametric uncertainty is obtained. Today, rehabilitation robots have an essential role in assisting physiotherapists in repairing muscle injuries. Since the uncertainties in rehabilitation robots change with practice in iteration, it is crucial to eliminate the effect of variable parametric uncertainty with iteration. Also, parametric uncertainty in the input and output matrices of a rehabilitation robot model directly affects the convergence of the ILC algorithm, and a small effect on each of these matrices may lead to algorithm divergence. In this paper, first, the law of the ILC algorithm without uncertainty is obtained. Then the robust convergence of this algorithm with a fixed learning gain in the presence of parametric uncertainty is proved. Finally, validation of the results obtained on a rehabilitation robot model is analyzed and evaluated.

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