Overcoming Output Constraints in Iterative Learning Control Systems by Reference Adaptation
Systems and Control
2021-08-12 v1 Systems and Control
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.
Cite
@article{arxiv.2002.00662,
title = {Overcoming Output Constraints in Iterative Learning Control Systems by Reference Adaptation},
author = {Michael Meindl and Fabio Molinari and Jörg Raisch and Thomas Seel},
journal= {arXiv preprint arXiv:2002.00662},
year = {2021}
}
Comments
Submitted to IFAC World Congress 2020