Overcoming output constraints in iterative learning control systems by reference adaptation

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Autoren

  • Michael Meindl
  • Fabio Molinari
  • Jörg Raisch
  • Thomas Seel
Forschungs-netzwerk anzeigen

Details

OriginalspracheEnglisch
Seiten (von - bis)1480-1486
Seitenumfang7
FachzeitschriftIFAC-PapersOnLine
Jahrgang53
Ausgabenummer2
PublikationsstatusVeröffentlicht - 2020

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.

ASJC Scopus Sachgebiete

Zitieren

Overcoming output constraints in iterative learning control systems by reference adaptation. / Meindl, Michael; Molinari, Fabio; Raisch, Jörg et al.
in: IFAC-PapersOnLine, Jahrgang 53, Nr. 2, 2020, S. 1480-1486.

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Meindl, M, Molinari, F, Raisch, J & Seel, T 2020, 'Overcoming output constraints in iterative learning control systems by reference adaptation', IFAC-PapersOnLine, Jg. 53, Nr. 2, S. 1480-1486. https://doi.org/10.1016/j.ifacol.2020.12.1938
Meindl, M., Molinari, F., Raisch, J., & Seel, T. (2020). Overcoming output constraints in iterative learning control systems by reference adaptation. IFAC-PapersOnLine, 53(2), 1480-1486. https://doi.org/10.1016/j.ifacol.2020.12.1938
Meindl M, Molinari F, Raisch J, Seel T. Overcoming output constraints in iterative learning control systems by reference adaptation. IFAC-PapersOnLine. 2020;53(2):1480-1486. doi: 10.1016/j.ifacol.2020.12.1938
Meindl, Michael ; Molinari, Fabio ; Raisch, Jörg et al. / Overcoming output constraints in iterative learning control systems by reference adaptation. in: IFAC-PapersOnLine. 2020 ; Jahrgang 53, Nr. 2. S. 1480-1486.
Download
@article{2c0026dbb6d14afba847d8c78b4c4667,
title = "Overcoming output constraints in iterative learning control systems by reference adaptation",
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.",
keywords = "Control of constrained systems, Iterative and Repetitive learning control, Learning for control, Linear systems",
author = "Michael Meindl and Fabio Molinari and J{\"o}rg Raisch and Thomas Seel",
year = "2020",
doi = "10.1016/j.ifacol.2020.12.1938",
language = "English",
volume = "53",
pages = "1480--1486",
number = "2",

}

Download

TY - JOUR

T1 - Overcoming output constraints in iterative learning control systems by reference adaptation

AU - Meindl, Michael

AU - Molinari, Fabio

AU - Raisch, Jörg

AU - Seel, Thomas

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

KW - Control of constrained systems

KW - Iterative and Repetitive learning control

KW - Learning for control

KW - Linear systems

UR - http://www.scopus.com/inward/record.url?scp=85107788920&partnerID=8YFLogxK

U2 - 10.1016/j.ifacol.2020.12.1938

DO - 10.1016/j.ifacol.2020.12.1938

M3 - Article

VL - 53

SP - 1480

EP - 1486

JO - IFAC-PapersOnLine

JF - IFAC-PapersOnLine

SN - 2405-8963

IS - 2

ER -