The University of Southampton
University of Southampton Institutional Repository

Iterative learning control of stochastic linear systems with reference trajectory switching

Iterative learning control of stochastic linear systems with reference trajectory switching
Iterative learning control of stochastic linear systems with reference trajectory switching
A reference trajectory is specified for systems that repetitively execute the same finite duration task in iterative learning control. In many current designs, the reference signal remains the same, but in others, it is desirable to allow the reference trajectory to change during the system’s overall operation. This paper develops a control law design method for linear dynamics where the measured signals are noise corrupted, random disturbances are present, and the reference trajectory is allowed to change during operation. The new design is based on the recently developed stochastic stability theory for repetitive processes, a class of 2D systems, and uses vector Lyapunov functions and their divergence properties. It also shows how to eliminate the transient error that results from a switch of the reference trajectory. A numerical case study demonstrates the applicability of the new design.
6572-6577
Pakshin, Pavel
8bee1030-fcdf-4e47-abca-72b2d07fd20a
Emelianova, Julia
04343da6-8438-40e3-b128-fc773905ea16
Rogers, Eric
611b1de0-c505-472e-a03f-c5294c63bb72
Galkowski, Krzysztof
322994ac-7e24-4350-ab72-cc80ac8078ef
Pakshin, Pavel
8bee1030-fcdf-4e47-abca-72b2d07fd20a
Emelianova, Julia
04343da6-8438-40e3-b128-fc773905ea16
Rogers, Eric
611b1de0-c505-472e-a03f-c5294c63bb72
Galkowski, Krzysztof
322994ac-7e24-4350-ab72-cc80ac8078ef

Pakshin, Pavel, Emelianova, Julia, Rogers, Eric and Galkowski, Krzysztof (2021) Iterative learning control of stochastic linear systems with reference trajectory switching. In 60th IEEE Conference on Decision and Control (CDC). pp. 6572-6577 . (doi:10.1109/CDC45484.2021.9682991).

Record type: Conference or Workshop Item (Paper)

Abstract

A reference trajectory is specified for systems that repetitively execute the same finite duration task in iterative learning control. In many current designs, the reference signal remains the same, but in others, it is desirable to allow the reference trajectory to change during the system’s overall operation. This paper develops a control law design method for linear dynamics where the measured signals are noise corrupted, random disturbances are present, and the reference trajectory is allowed to change during operation. The new design is based on the recently developed stochastic stability theory for repetitive processes, a class of 2D systems, and uses vector Lyapunov functions and their divergence properties. It also shows how to eliminate the transient error that results from a switch of the reference trajectory. A numerical case study demonstrates the applicability of the new design.

This record has no associated files available for download.

More information

Published date: 13 December 2021

Identifiers

Local EPrints ID: 454886
URI: http://eprints.soton.ac.uk/id/eprint/454886
PURE UUID: 663df13d-1649-48d2-9e84-57f647bebb7a
ORCID for Eric Rogers: ORCID iD orcid.org/0000-0003-0179-9398

Catalogue record

Date deposited: 01 Mar 2022 17:36
Last modified: 17 Mar 2024 02:37

Export record

Altmetrics

Contributors

Author: Pavel Pakshin
Author: Julia Emelianova
Author: Eric Rogers ORCID iD
Author: Krzysztof Galkowski

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.

×