The University of Southampton
University of Southampton Institutional Repository

Norm Optimal Iterative Learning Control with Auxiliary Optimization: A Switching Approach

Owens, D H, Freeman, C T and Chu, Bing (2013) Norm Optimal Iterative Learning Control with Auxiliary Optimization: A Switching Approach At IFAC International Workshop on Adaptation and Learning in Control and Signal Processing 2013, France. 03 - 05 Jul 2013. , pp. 140-145.

Record type: Conference or Workshop Item (Paper)

Abstract

The paper describes a substantial extension of Norm Optimal Iterative Learning Control (NOILC) that permits tracking of a class of finite dimensional reference signals whilst simultaneously minimizing a quadratic cost function. Motivated by practical problems in automation and control, this enables point-to-point motion tasks to be performed whilst reducing effects such as payload spillage, vibration tendencies and actuator wear. Solutions combine feedforward and feedback actions, and are experimentally tested using a robotic arm.

Full text not available from this repository.

More information

Published date: 3 July 2013
Venue - Dates: IFAC International Workshop on Adaptation and Learning in Control and Signal Processing 2013, France, 2013-07-03 - 2013-07-05
Organisations: EEE, Southampton Wireless Group

Identifiers

Local EPrints ID: 344300
URI: http://eprints.soton.ac.uk/id/eprint/344300
PURE UUID: f29dda27-a50f-42e1-8126-45d147912ac3
ORCID for Bing Chu: ORCID iD orcid.org/0000-0002-2711-8717

Catalogue record

Date deposited: 16 Oct 2012 23:29
Last modified: 18 Jul 2017 05:17

Export record

Contributors

Author: D H Owens
Author: C T Freeman
Author: Bing Chu ORCID iD

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.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

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.

×