Overcoming output constraints in iterative learning control systems by reference adaptation

verfasst von

Michael Meindl, Fabio Molinari, Jörg Raisch, Thomas Seel

Abstract

Iterative Learning Control (ILC) schemes can guarantee properties such as asymptotic stability and monotonic error convergence, but do not, in general, ensure adherence to output constraints. The topic of this paper is the design of a reference-adapting ILC (RAILC) scheme, extending an existing ILC system and capable of complying with output constraints. The underlying idea is to scale the reference at every trial by using a conservative estimate of the output's progression. Properties as the monotonic convergence above a threshold and the respect of output constraints are formally proven. Numerical simulations and experimental results reinforce our theoretical results.

Organisationseinheit(en)

Institut für Mechatronische Systeme
Robotik & autonome Systeme

Typ

Artikel

Journal

IFAC-PapersOnLine

Band

53

Seiten

1480-1486

Anzahl der Seiten

7

ISSN

2405-8963

Publikationsdatum

2020

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Steuerungs- und Systemtechnik

Elektronische Version(en)

https://doi.org/10.1016/j.ifacol.2020.12.1938 (Zugang: Offen)