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Article: Neurofuzzy network based self-tuning control with offset eliminating

TitleNeurofuzzy network based self-tuning control with offset eliminating
Authors
Issue Date2003
PublisherTaylor & Francis Ltd. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/00207721.asp
Citation
International Journal Of Systems Science, 2003, v. 34 n. 2, p. 111-122 How to Cite?
AbstractThe design of nonlinear controllers involves first selecting the input and then determining the nonlinear functions for the controllers. Since systems described by smooth nonlinear functions can be approximated by linear models in the neighbourhood of the selected operating points, the input of the nonlinear controller at these operating points can be chosen to be identical to those of the local linear controllers. Following this approach, it is proposed that the input of the nonlinear controller are similarly chosen, and that the local linear controllers are designed based on the integrating and k-incremental suboptimal control laws for their ability to remove offsets. Neurofuzzy networks are used to implement the nonlinear controllers for their ability to approximate nonlinear functions with arbitrary accuracy, and to be trained from experimental data. These nonlinear controllers are referred to as neurofuzzy controllers for convenience. As the integrating and k-incremental control laws have also been applied to implement self-tuning controllers, the proposed neurofuzzy controllers can also be interpreted as self-tuning nonlinear controllers. The training target for the neurofuzzy controllers is derived, and online training of the neurofuzzy controllers using a simplified recursive least squares (SRLS) method is presented. It is shown that using the SRLS method, computing time to train the neurofuzzy controllers can be drastically reduced and the ability to track varying dynamics improved. The performance of the neurofuzzy controllers and their ability to remove offsets are demonstrated by two simulation examples involving a linear and a nonlinear system, and a case study involving the control of the drum water level in the boiler of a power generation system.
Persistent Identifierhttp://hdl.handle.net/10722/76151
ISSN
2015 Impact Factor: 1.947
2015 SCImago Journal Rankings: 1.083
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorChan, CWen_HK
dc.contributor.authorLiu, XJen_HK
dc.contributor.authorYeung, WKen_HK
dc.date.accessioned2010-09-06T07:18:08Z-
dc.date.available2010-09-06T07:18:08Z-
dc.date.issued2003en_HK
dc.identifier.citationInternational Journal Of Systems Science, 2003, v. 34 n. 2, p. 111-122en_HK
dc.identifier.issn0020-7721en_HK
dc.identifier.urihttp://hdl.handle.net/10722/76151-
dc.description.abstractThe design of nonlinear controllers involves first selecting the input and then determining the nonlinear functions for the controllers. Since systems described by smooth nonlinear functions can be approximated by linear models in the neighbourhood of the selected operating points, the input of the nonlinear controller at these operating points can be chosen to be identical to those of the local linear controllers. Following this approach, it is proposed that the input of the nonlinear controller are similarly chosen, and that the local linear controllers are designed based on the integrating and k-incremental suboptimal control laws for their ability to remove offsets. Neurofuzzy networks are used to implement the nonlinear controllers for their ability to approximate nonlinear functions with arbitrary accuracy, and to be trained from experimental data. These nonlinear controllers are referred to as neurofuzzy controllers for convenience. As the integrating and k-incremental control laws have also been applied to implement self-tuning controllers, the proposed neurofuzzy controllers can also be interpreted as self-tuning nonlinear controllers. The training target for the neurofuzzy controllers is derived, and online training of the neurofuzzy controllers using a simplified recursive least squares (SRLS) method is presented. It is shown that using the SRLS method, computing time to train the neurofuzzy controllers can be drastically reduced and the ability to track varying dynamics improved. The performance of the neurofuzzy controllers and their ability to remove offsets are demonstrated by two simulation examples involving a linear and a nonlinear system, and a case study involving the control of the drum water level in the boiler of a power generation system.en_HK
dc.languageengen_HK
dc.publisherTaylor & Francis Ltd. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/00207721.aspen_HK
dc.relation.ispartofInternational Journal of Systems Scienceen_HK
dc.titleNeurofuzzy network based self-tuning control with offset eliminatingen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0020-7721&volume=34&issue=2&spage=111&epage=122&date=2003&atitle=Neurofuzzy+network+based+self-tuning+control+with+offset+eliminatingen_HK
dc.identifier.emailChan, CW: mechan@hkucc.hku.hken_HK
dc.identifier.authorityChan, CW=rp00088en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1080/0020772031000115551en_HK
dc.identifier.scopuseid_2-s2.0-0142217285en_HK
dc.identifier.hkuros79469en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0142217285&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume34en_HK
dc.identifier.issue2en_HK
dc.identifier.spage111en_HK
dc.identifier.epage122en_HK
dc.identifier.isiWOS:000184140400003-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridChan, CW=7404814060en_HK
dc.identifier.scopusauthoridLiu, XJ=37045874400en_HK
dc.identifier.scopusauthoridYeung, WK=24345897100en_HK

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