Title: Optimal iterative learning control for a class of non-minimum phase systems

Authors: Leila Noueili; Wassila Chagra; Moufida Lahmari Ksouri

Addresses: Laboratoire Analyse, Conception et Commande des Systèmes LR11ES20, École Nationale d'Ingénieurs de Tunis, Université de Tunis El Manar, Tunisia ' Laboratoire Analyse, Conception et Commande des Systèmes LR11ES20, Institut Préparatoire aux Etudes d'Ingénieurs d'El Manar, Université de Tunis El Manar, Tunisia ' Laboratoire Analyse, Conception et Commande des Systèmes LR11ES20, École Nationale d'Ingénieurs de Tunis Université de Tunis El Manar, Tunisia

Abstract: In this paper, an optimal iterative learning control (ILC) approach is proposed for a class of repetitive non-minimum phase (NMP) systems. The control law synthesis is based on the resolution of a quadratic criterion which minimises the errors between the setpoint references and the system outputs at each iteration for each trial. The resolution of the control problem uses a new gain which avoids matricial inversion problems appearing in classical ILC algorithms such as direct model inversion (I-ILC) and optimal ILC (Q-ILC). The new optimal ILC approach improves the learning convergence significantly compared to the previously mentioned algorithms. Furthermore, sufficient and necessary stability conditions are established with convergence properties. The effectiveness of the proposed method is proved by simulations with an NMP mass-spring damper system.

Keywords: learning control; inverse model ILC; optimal ILC; alpha-ILC; MIMO systems; non-minimum phase systems.

DOI: 10.1504/IJMIC.2017.086564

International Journal of Modelling, Identification and Control, 2017 Vol.28 No.3, pp.284 - 294

Received: 05 Feb 2016
Accepted: 08 Oct 2016
Published online: 12 Sep 2017 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article