Controller assessment for a class of non-linear systems

Show simple item record Harris, TJ en Yu, Wei en 2012-03-19T02:41:55Z en 2007 en
dc.identifier.citation Journal of Process Control 17:607-619 2007 en
dc.identifier.issn 0959-1524 en
dc.identifier.uri en
dc.description.abstract The use of autoregressive moving average (ARMA) models to assess the control loop performance for processes that are adequately described by the superposition of a linear dynamic model and linear stochastic or deterministic disturbance model is well known. In this paper, classes of non-linear dynamic/stochastic systems for which a similar result can be obtained are established for single-input singleoutput discrete system. For these systems, lower mean-square error bounds on performance, can be estimated from the closed-loop routine operating data by using non-linear autoregressive moving average with exogenous inputs (NARMAX) models. It is necessary to know the process time delay. The fitting of these models is greatly facilitated by using efficient algorithms, such as Orthogonal Least Squares or other fast orthogonal search algorithms. These models can also be used to assess the predictive importance of non-linearities over multiple-time horizons. en
dc.publisher Elsevier en
dc.relation.ispartofseries Journal of Process Control en
dc.rights Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. Details obtained from: en
dc.rights.uri en
dc.title Controller assessment for a class of non-linear systems en
dc.type Journal Article en
dc.identifier.doi 10.1016/j.jprocont.2007.01.011 en
pubs.begin-page 607 en
pubs.volume 17 en
dc.rights.holder Copyright: Elsevier en
pubs.end-page 619 en
dc.rights.accessrights en
pubs.subtype Article en
pubs.elements-id 319743 en Engineering en Chemical and Materials Eng en
pubs.record-created-at-source-date 2012-03-13 en

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