The University of Southampton
University of Southampton Institutional Repository

ILC Initial Input Selection with Experimental Verification

ILC Initial Input Selection with Experimental Verification
ILC Initial Input Selection with Experimental Verification
Error convergence in Iterative Learning Control (ILC) is generally highly dependent on the selection of the initial input signal applied to the system. Techniques for generation of an initial choice of input are therefore considered in this paper, based on i) a frequency-domain model-based approach, ii) a time-domain model-free method involving use of previously stored tasks and their associated convergent input demands, and iii) a combination of these approaches. Each is shown to significantly decrease the error over subsequent trials using a common form of linear ILC algorithm compared with a more arbitrary initial input selection. Experimental results are then presented using a gantry robot test facility in order to establish the efficacy and practical applicability of each technique.
Alsubaie, Muhammad A.
0ac92387-d118-4251-85fd-0e8affb77eea
Freeman, Christopher
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Cai, Zhonglun
dd8dd525-19a5-4792-a048-617340996afe
Lewin, Paul
78b4fc49-1cb3-4db9-ba90-3ae70c0f639e
Rogers, Eric
611b1de0-c505-472e-a03f-c5294c63bb72
Alsubaie, Muhammad A.
0ac92387-d118-4251-85fd-0e8affb77eea
Freeman, Christopher
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Cai, Zhonglun
dd8dd525-19a5-4792-a048-617340996afe
Lewin, Paul
78b4fc49-1cb3-4db9-ba90-3ae70c0f639e
Rogers, Eric
611b1de0-c505-472e-a03f-c5294c63bb72

Alsubaie, Muhammad A., Freeman, Christopher, Cai, Zhonglun, Lewin, Paul and Rogers, Eric (2009) ILC Initial Input Selection with Experimental Verification. Symposium on Learning Control at IEEE CDC 2009. 14 - 15 Dec 2009.

Record type: Conference or Workshop Item (Paper)

Abstract

Error convergence in Iterative Learning Control (ILC) is generally highly dependent on the selection of the initial input signal applied to the system. Techniques for generation of an initial choice of input are therefore considered in this paper, based on i) a frequency-domain model-based approach, ii) a time-domain model-free method involving use of previously stored tasks and their associated convergent input demands, and iii) a combination of these approaches. Each is shown to significantly decrease the error over subsequent trials using a common form of linear ILC algorithm compared with a more arbitrary initial input selection. Experimental results are then presented using a gantry robot test facility in order to establish the efficacy and practical applicability of each technique.

PDF
Prediction_1_conference_final_CTF_+5.pdf - Version of Record
Restricted to Registered users only
Download (523kB)
Request a copy

More information

Published date: 14 December 2009
Additional Information: Event Dates: December 14-15, 2009
Venue - Dates: Symposium on Learning Control at IEEE CDC 2009, 2009-12-14 - 2009-12-15
Organisations: EEE, Southampton Wireless Group

Identifiers

Local EPrints ID: 267637
URI: https://eprints.soton.ac.uk/id/eprint/267637
PURE UUID: 7dbed9ab-5c0e-4b2d-a622-a1a21326220b

Catalogue record

Date deposited: 30 Jun 2009 09:24
Last modified: 18 Jul 2017 07:01

Export record

Contributors

Author: Muhammad A. Alsubaie
Author: Zhonglun Cai
Author: Paul Lewin
Author: Eric Rogers

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 https://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.

×