The University of Southampton
University of Southampton Institutional Repository

Higher-order iterative learning control law design using linear repetitive process theory: convergence and robustness

Higher-order iterative learning control law design using linear repetitive process theory: convergence and robustness
Higher-order iterative learning control law design using linear repetitive process theory: convergence and robustness
Iterative learning control has been developed for processes or systems that complete the same finite duration task over and over again. The mode of operation is that after each execution is complete the system resets to the starting location, the next execution is completed and so on. Each execution is known as a trial and its duration is termed the trial length. Once each trial is complete the information generated is available for use in computing the control input for the next trial. This paper uses the repetitive process setting to develop new results on the design of higher-order ILC control laws for discrete dynamics. The new results include conditions that guarantee error convergence and design in the presence of model uncertainty.
2405-8963
3123-3128
Wang, Xuan
90f96092-6c02-4002-897b-a1e983ca3b56
Chu, Bing
555a86a5-0198-4242-8525-3492349d4f0f
Rogers, Eric
611b1de0-c505-472e-a03f-c5294c63bb72
Wang, Xuan
90f96092-6c02-4002-897b-a1e983ca3b56
Chu, Bing
555a86a5-0198-4242-8525-3492349d4f0f
Rogers, Eric
611b1de0-c505-472e-a03f-c5294c63bb72

Wang, Xuan, Chu, Bing and Rogers, Eric (2017) Higher-order iterative learning control law design using linear repetitive process theory: convergence and robustness. IFAC-PapersOnLine, 50 (1), 3123-3128. (doi:10.1016/j.ifacol.2017.08.320).

Record type: Article

Abstract

Iterative learning control has been developed for processes or systems that complete the same finite duration task over and over again. The mode of operation is that after each execution is complete the system resets to the starting location, the next execution is completed and so on. Each execution is known as a trial and its duration is termed the trial length. Once each trial is complete the information generated is available for use in computing the control input for the next trial. This paper uses the repetitive process setting to develop new results on the design of higher-order ILC control laws for discrete dynamics. The new results include conditions that guarantee error convergence and design in the presence of model uncertainty.

Text
Higher-order Iterative Learning Control Law Design using Linear Reptitive Process Theory: Convergence and Robustness - Accepted Manuscript
Download (281kB)

More information

Accepted/In Press date: 27 February 2017
e-pub ahead of print date: 18 October 2017
Venue - Dates: 20th IFAC World congress, France, 2017-07-09 - 2017-07-14

Identifiers

Local EPrints ID: 415683
URI: http://eprints.soton.ac.uk/id/eprint/415683
ISSN: 2405-8963
PURE UUID: 36d53fd4-07b6-4c4f-8ea1-1abed5bc9077
ORCID for Bing Chu: ORCID iD orcid.org/0000-0002-2711-8717
ORCID for Eric Rogers: ORCID iD orcid.org/0000-0003-0179-9398

Catalogue record

Date deposited: 20 Nov 2017 17:30
Last modified: 07 Oct 2020 05:35

Export record

Altmetrics

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.

×