Adaptive Model Predictive Control for Wiener Nonlinear Systems

Wiener model, which is one of the structures used in the modeling of nonlinear systems, consists of the cascade connection as that a dynamic linear system is followed in series by a static nonlinear function. Different approaches have been developed and proposed to control this kind of systems in the last decades. In this study, a model predictive control system with online identification support has been developed. The prominent feature of online system identification may be referred to as accommodating easily to severe changes in system parameters. The combination of MPC algorithm with online identification constitutes an adaptive model predictive control algorithm that can sense the input parameter variation. To assess the performance of the proposed control system, a strong acid–strong base chemical neutralization process without buffer solution is selected, and the controller is applied to the chemical process to verify its effectiveness in acidic, alkaline and neutral regions. Results obtained from MATLAB/Simulink studies confirm the performance of the controller that serves under variable system conditions. © 2018, Shiraz University.

Dergi Adı Iranian Journal of Science and Technology - Transactions of Electrical Engineering
Dergi Cilt Bilgisi 43
Sayfalar 361 - 377
Yayın Yılı 2019
Eser Adı
[dc.title]
Adaptive Model Predictive Control for Wiener Nonlinear Systems
Yazar
[dc.contributor.author]
Aliskan I.
Yayın Yılı
[dc.date.issued]
2019
Yayıncı
[dc.publisher]
Springer International Publishing
Yayın Türü
[dc.type]
article
Özet
[dc.description.abstract]
Wiener model, which is one of the structures used in the modeling of nonlinear systems, consists of the cascade connection as that a dynamic linear system is followed in series by a static nonlinear function. Different approaches have been developed and proposed to control this kind of systems in the last decades. In this study, a model predictive control system with online identification support has been developed. The prominent feature of online system identification may be referred to as accommodating easily to severe changes in system parameters. The combination of MPC algorithm with online identification constitutes an adaptive model predictive control algorithm that can sense the input parameter variation. To assess the performance of the proposed control system, a strong acid–strong base chemical neutralization process without buffer solution is selected, and the controller is applied to the chemical process to verify its effectiveness in acidic, alkaline and neutral regions. Results obtained from MATLAB/Simulink studies confirm the performance of the controller that serves under variable system conditions. © 2018, Shiraz University.
Kayıt Giriş Tarihi
[dc.date.accessioned]
2019-12-23
Açık Erişim Tarihi
[dc.date.available]
2019-12-23
Yayın Dili
[dc.language.iso]
eng
Konu Başlıkları
[dc.subject]
Least squares
Konu Başlıkları
[dc.subject]
Parameter estimation
Konu Başlıkları
[dc.subject]
Predictive control
Konu Başlıkları
[dc.subject]
Wiener system
Haklar
[dc.rights]
info:eu-repo/semantics/closedAccess
ISSN
[dc.identifier.issn]
2228-6179
İlk Sayfa Sayısı
[dc.identifier.startpage]
361
Son Sayfa Sayısı
[dc.identifier.endpage]
377
Dergi Adı
[dc.relation.journal]
Iranian Journal of Science and Technology - Transactions of Electrical Engineering
Dergi Cilt Bilgisi
[dc.identifier.volume]
43
Tek Biçim Adres
[dc.identifier.uri]
https://dx.doi.org/10.1007/s40998-018-0159-0
Tek Biçim Adres
[dc.identifier.uri]
https://hdl.handle.net/20.500.12628/4134
Görüntülenme Sayısı ( Şehir )
Görüntülenme Sayısı ( Ülke )
Görüntülenme Sayısı ( Zaman Dağılımı )
Görüntülenme
114
09.12.2022 tarihinden bu yana
İndirme
1
09.12.2022 tarihinden bu yana
Son Erişim Tarihi
15 Temmuz 2024 20:24
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Tıklayınız
system control identification online proposed developed process predictive chemical systems algorithm nonlinear controller performance parameter variation assess strong selected solution acid–strong buffer without neutralization Wiener applied studies University Shiraz conditions variable serves confirm MATLAB/Simulink verify
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