Non-linear multiple model switched iterative learning control
Non-linear multiple model switched iterative learning control
This paper develops a multiple model switched iterative learning control (ILC) framework for a general class of nonlinear plant dynamics. Given a set of candidate plant models chosen by the designer as possible representations of the true plant, the switching framework converges to a bounded tracking error whose norm is proportional to the (modified)gap metric between the true plant and the closest model in the candidate model set. This is the first multiple model switched ILC framework to provide guaranteed tracking performance for non-linear systems using a general class of ILC update(including Newton, norm-optimal, and gradient types) subject to unstructured model uncertainty. The transparent design framework is illustrated through application of the framework to a simulation example which demonstrates precise tracking error in the presence of arbitrarily large plant uncertainty.
Hodgins, Lucy
2cb70295-f4b0-4c0d-ba23-43fc531b9392
Freeman, Chris
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Belkhatir, Zehor
de90d742-a58f-4425-837c-20ff960fb9b6
9 December 2025
Hodgins, Lucy
2cb70295-f4b0-4c0d-ba23-43fc531b9392
Freeman, Chris
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Belkhatir, Zehor
de90d742-a58f-4425-837c-20ff960fb9b6
Hodgins, Lucy, Freeman, Chris and Belkhatir, Zehor
(2025)
Non-linear multiple model switched iterative learning control.
64th IEEE Conference on Decision and Control, The Windsor Convention Center, Rio de Janeiro, Brazil.
09 - 12 Dec 2025.
6 pp
.
Record type:
Conference or Workshop Item
(Paper)
Abstract
This paper develops a multiple model switched iterative learning control (ILC) framework for a general class of nonlinear plant dynamics. Given a set of candidate plant models chosen by the designer as possible representations of the true plant, the switching framework converges to a bounded tracking error whose norm is proportional to the (modified)gap metric between the true plant and the closest model in the candidate model set. This is the first multiple model switched ILC framework to provide guaranteed tracking performance for non-linear systems using a general class of ILC update(including Newton, norm-optimal, and gradient types) subject to unstructured model uncertainty. The transparent design framework is illustrated through application of the framework to a simulation example which demonstrates precise tracking error in the presence of arbitrarily large plant uncertainty.
Text
CDC2025 (2)
- Accepted Manuscript
Restricted to Repository staff only until 9 December 2025.
Request a copy
More information
Published date: 9 December 2025
Venue - Dates:
64th IEEE Conference on Decision and Control, The Windsor Convention Center, Rio de Janeiro, Brazil, 2025-12-09 - 2025-12-12
Identifiers
Local EPrints ID: 506065
URI: http://eprints.soton.ac.uk/id/eprint/506065
PURE UUID: 01f8c34e-74e8-495d-84aa-51c3ddd17351
Catalogue record
Date deposited: 28 Oct 2025 17:40
Last modified: 04 Nov 2025 03:07
Export record
Contributors
Author:
Lucy Hodgins
Author:
Chris Freeman
Author:
Zehor Belkhatir
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics