Details
| Original language | English |
|---|---|
| Pages (from-to) | 1480-1486 |
| Number of pages | 7 |
| Journal | IFAC-PapersOnLine |
| Volume | 53 |
| Issue number | 2 |
| Publication status | Published - 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.
Keywords
- Control of constrained systems, Iterative and Repetitive learning control, Learning for control, Linear systems
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
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In: IFAC-PapersOnLine, Vol. 53, No. 2, 2020, p. 1480-1486.
Research output: Contribution to journal › Article › Research › peer review
}
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 -