High order iterative learning control to solve the trajectory tracking problem for robot manipulators using Lyapunov theory. (November 2018)

• Record Type: Journal Article
• Authors: Bouakrif, Farah; Zasadzinski, Michel
• Is Part Of: Transactions of the Institute of Measurement and Control. Volume 40:Number 15(2018)
• Page Start: 4105
• Page End: 4114
• Publication Date: 2018-11
• Subjects: Iterative learning control -- high order ILC -- Lyapunov theory -- asymptotic stability -- robot manipulator; Automatic control -- Periodicals; Measuring instruments -- Periodicals; Commande automatique -- Périodiques; Mesure -- Instruments -- Périodiques; 681.2
• DOI: 10.1177/0142331217741958
• Languages: English
• ISSNs: 0142-3312

Abstract

This paper deals with Iterative Learning Control (ILC) design to solve the trajectory tracking problem for rigid robot manipulators subject to external disturbances, and performing repetitive tasks. A high order ILC scheme is synthetized; this controller contains the information (errors) of several iterations and not only of one iteration. It has been shown that the closed loop system (robot plus controller) is asymptotically stable, over the whole finite time interval, when the iteration number tends to infinity. This proof is based upon the use of a Lyapunov-like positive definite sequence, which is shown to be monotonically decreasing under the proposed controller scheme. Finally, simulation results on two-link manipulator are provided to illustrate the effectiveness of the proposed controller.