The University of Southampton
University of Southampton Institutional Repository

Norm optimal iterative learning control based on a multiple model switched adaptive framework

Brend, O., Freeman, C.T. and French, M. (2013) Norm optimal iterative learning control based on a multiple model switched adaptive framework At IEEE 52nd Annual Conference on Decision and Control, Italy. 10 - 13 Dec 2013. 6 pp, pp. 7297-7302.

Record type: Conference or Workshop Item (Paper)


In this paper a prominent class of iterative learning control (ILC) algorithm is reformulated in the framework of estimation-based multiple model switched adaptive control (EMMSAC). The resulting control scheme uses a bank of Kalman filters to assess the performance of a set of candidate plant models, and the ILC update at the end of each trial is constructed using the plant model with smallest residual. Through exploitation of the powerful underlying EMMSAC framework, rigorous bounds are available to guarantee robust ILC performance without placing constraints on the form of uncertainty or control action.

This paper hence addresses current limitations in ILC approaches for uncertain systems which are typically highly restrictive in the form or magnitude of the uncertainty, employ prescribed controller forms, or, alternatively are heuristically motivated with no theoretical stability/performance guarantees. Experimental results from a highly relevant application of ILC in stroke rehabilitation are given to confirm the efficacy and scope of the framework.

PDF CDC13_1792_FI.pdf - Author's Original
Restricted to Registered users only
Download (667kB)

More information

Published date: 1 October 2013
Venue - Dates: IEEE 52nd Annual Conference on Decision and Control, Italy, 2013-12-10 - 2013-12-13
Organisations: EEE, Southampton Wireless Group


Local EPrints ID: 354854
PURE UUID: c199e40e-3e76-4059-a781-c149b1e8d274

Catalogue record

Date deposited: 19 Jul 2013 19:33
Last modified: 18 Jul 2017 03:52

Export record


Author: O. Brend
Author: C.T. Freeman
Author: M. French

University divisions

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

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton:

ePrints Soton supports OAI 2.0 with a base URL of

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.