Adaptive robust iterative learning control with application to a Delta robot. (February 2021)

• Record Type: Journal Article
• Authors: Boudjedir, Chems Eddine; Boukhetala, Djamel
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 235:Number 2(2021)
• Page Start: 207
• Page End: 221
• Publication Date: 2021-02
• Subjects: Iterative learning -- adaptive control -- alignment condition -- trajectory tracking -- Delta robot; Mechanical engineering -- Periodicals; Automatic control -- Periodicals; Systems engineering -- Periodicals; 621.3
• DOI: 10.1177/0959651820938531
• Languages: English
• ISSNs: 0959-6518

Abstract

In this article, an adaptive robust iterative learning control is developed to solve the trajectory tracking problem of a parallel Delta robot performing repetitive tasks and subjected to external disturbances. The proposed control scheme is composed of an adaptive proportional–derivative controller to increase the convergence rate, a proportional–derivative-type iterative learning control to enhance the tracking performances through the repetitive trajectory as well as a robust term to compensate the repetitive and nonrepetitive disturbances. The practical assumption of alignment condition is introduced instead of the classical assumption of resetting conditions. The asymptotic convergence is proved using Lyaponuv analysis, and it is shown that the tracking error decreases through the iterations. Simulation and experiments are performed on a Delta robot to demonstrate the effectiveness and the superiority of the proposed controller over the traditional iterative learning control.