Skip navigation
Skip navigation

Model Validation for Control and Controller Validation in a Prediction error Identification Framework - Part I

Gevers, Michel; Bombois, Xavier; Codrons, Bonoit; Scorletti, Gérard; Anderson, Brian

Description

We propose a model validation procedure that consists of a prediction error identification experiment with a full order model. It delivers a parametric uncertainty ellipsoid and a corresponding set of parameterized transfer functions, which we call prediction error (PE) uncertainty set. Such uncertainty set differs from the classical uncertainty descriptions used in robust control analysis and design. We develop a robust control analysis theory for such uncertainty sets, which covers two...[Show more]

dc.contributor.authorGevers, Michel
dc.contributor.authorBombois, Xavier
dc.contributor.authorCodrons, Bonoit
dc.contributor.authorScorletti, Gérard
dc.contributor.authorAnderson, Brian
dc.date.accessioned2015-12-13T23:06:10Z
dc.date.available2015-12-13T23:06:10Z
dc.identifier.issn0005-1098
dc.identifier.urihttp://hdl.handle.net/1885/85902
dc.description.abstractWe propose a model validation procedure that consists of a prediction error identification experiment with a full order model. It delivers a parametric uncertainty ellipsoid and a corresponding set of parameterized transfer functions, which we call prediction error (PE) uncertainty set. Such uncertainty set differs from the classical uncertainty descriptions used in robust control analysis and design. We develop a robust control analysis theory for such uncertainty sets, which covers two distinct aspects: (1) Controller validation. We present necessary and sufficient conditions for a specific controller to stabilize - or to achieve a given level of performance with - all systems in such PE uncertainty set. (2) Model validation for robust control. We present a measure for the size of such PE uncertainty set that is directly connected to the size of a set controllers that stabilize all systems in the model uncertainty set. This allows us to establish that one uncertainty set is better tuned for robust control design than another, leading to control-oriented validation objectives.
dc.publisherPergamon-Elsevier Ltd
dc.sourceAutomatica
dc.subjectKeywords: Control equipment; Error analysis; Identification (control systems); Predictive control systems; Robustness (control systems); Transfer functions; Error identification; Control system analysis Controller validation; Identification for robust control; Model validation; System identification
dc.titleModel Validation for Control and Controller Validation in a Prediction error Identification Framework - Part I
dc.typeJournal article
local.description.notesImported from ARIES
local.description.refereedYes
local.identifier.citationvolume39
dc.date.issued2003
local.identifier.absfor010203 - Calculus of Variations, Systems Theory and Control Theory
local.identifier.ariespublicationMigratedxPub14584
local.type.statusPublished Version
local.contributor.affiliationGevers, Michel, Catholic University of Louvain
local.contributor.affiliationBombois, Xavier, Delft University of Technology
local.contributor.affiliationCodrons, Bonoit, Catholic University of Louvain
local.contributor.affiliationScorletti, Gérard, LAP ISMRA
local.contributor.affiliationAnderson, Brian, College of Engineering and Computer Science, ANU
local.bibliographicCitation.issue3
local.bibliographicCitation.startpage403
local.bibliographicCitation.lastpage415
local.identifier.doi10.1016/S0005-1098(02)00234-0
dc.date.updated2015-12-12T08:04:11Z
local.identifier.scopusID2-s2.0-0037361682
CollectionsANU Research Publications

Download

There are no files associated with this item.


Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.

Updated:  19 May 2020/ Responsible Officer:  University Librarian/ Page Contact:  Library Systems & Web Coordinator