Konferans Bildirisi
BibTex RIS Kaynak Göster

Wiener Sistemlerinin Gradyan Tabanlı Tanımlaması

Yıl 2018, Cilt: 24 Sayı: 8, 1418 - 1424, 29.12.2018

Öz

Tek bir bloktan oluşan sistemlerde geribesleme ile
kontrol ve giriş-çıkış ilişkisinin kurulması klasik kontrolde yaygın olarak
görülmektedir.  Fakat doğal sistemler göz
önüne alındığında çok bloklu yapılar ve bu bloklar içerisinde de lineer olmayan
fonksiyonlar görülür. Bu çalışmanın konusu, giriş işaretinin lineer bloka
uygulandığı ve çıkışın lineer olmayan fonksiyondan alındığı sistem yapısı olan
Wiener tipi sistemlerin tanımlamasıdır. Durum geribeslemenin mümkün olmadığı bu
sistem tipinde tanımlama ile farklı kontrol algoritmalarının kullanımı
mümkündür. Sistem tanımlamada harici girişli otoregresif ağ (ARX)-polinom
kaskad bağlantısı tercihi ile en küçük kareler yöntemi ve eğim bilgileri
sayesinde sistemin giriş-çıkışı arasındaki matematiksel ilişki elde edilmiştir.
Üç farklı örnek sistem üzerinde çalışmalar yapılmış, MATLAB/Simulink ortamında
veri kümeleri elde edilmiş ve yapılan sistem tanımlamalarının başarımı grafikler
ile sunulmuştur.

Kaynakça

  • Jafari M, Salimifard M, Dehghani M. “Identification of multivariable nonlinear systems in the presence of colored noises using iterative hierarchical least squares Algorithm”. ISA Transactions, 53 (4), 1243-1252, 2014.
  • Salhi H, Kamoun S. “A recursive parametric estimation algorithm of multivariable nonlinear systems described by Hammerstein mathematical models”. Applied Mathematical Modelling, 39(16), 4951-4962, 2015.
  • Chang F, Luus R. “A nonlinear method for identification using Hammerstein model”. IEEE Transactions on Automatic Control, 16(5), 464-468, 1971.
  • Figueroa L, Cousseau JE, Werner S, Laakso T. “Adaptive control of a Wiener type system: application of a pH neutralization reactor”. Internaional Journal of Control, 80(2), 231-240, 2007.
  • Chi Q, Fei Z, Liu K, Liang J. “Latent-variable nonlinear model predictive control strategy for a pH neutralization process”. Asian Journal of Control, 17(6), 2427-2434, 2015.
  • Guo J, Yin LY, Zhao Y, Zhang JF. “Identification of Wiener systems with quantized inputs and binary-valued output observations”. Automatica, 78, 280-286, 2017.
  • Kibangou AY, Favier G. “Wiener-Hammerstein systems modeling using diagonal Volterra kernels coefficients”. IEEE Signal Processing Letter, 13(6), 381-384, 2006.
  • Lawrynjczuk M. “Nonlinear predictive control for Hammerstein-Wiener systems”. ISA Transactions, 55, 49-62, 2015.
  • Kazemi M, Arefi MM. “A fast iterative recursive least squares algorithm for Wiener model identification of highly nonlinear systems”. ISA Transactions, 67, 382-388, 2017.
  • Kalafatis A, Arifin N, Wang L, Cluett W. “A new approach to the identification of pH processes based on the Wiener model”. Chemical Engineering Science, 50(23), 3693-3701, 1995.
  • Zhang O, Ma Z, Li G, Qian Z, Guo X., “Temperature-dependent demagnetization nonlinear Wiener model with neural network for PM synchronous machines in electric vehicle”. 19th International Conference on Electrical Machines and Systems, Chiba, Japan, 13-16 November 2016.
  • Norquay SJ, Palazoglu A, Romagnoli JA. “Model predictive control based on Wiener models”. Chemical Engineering Science, 53(1), 75-84, 1998.
  • Kazemi M, Arefi MM. “Nonlinear generalized minimum variance control and control performance assessment of nonlinear systems based on a Wiener model”. Transactions of the Institute of Measurement and Control, https://doi.org/10.1177/0142331216685395.
  • Li S, Li Y. “Model predictive control of an intensified continuous reactor using a neural network Wiener model”. Neurocomputing, 185, 93-104, 2016.
  • Yu W, Pineda FJ. “Chemical process modelling with multiple neural networks”. European Control Conference, Porto, Portugal, 4-7 September 2001.
  • Zeydan M. “Bir petrol rafinerisi (TÜPRAŞ) akışkan yataklı katalitik parçalama ünitersinin (FCCU) bulanık modelleme uygulamaı”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 10(1), 101-110, 2002.
  • Ding F, Liu X, Liu M. “The recursive least squares identification algorithm for a class of Wiener nonlinear systems”. Journal of The Franklin Institute, 353(7), 1518-1526, 2016.
  • Liu M, Xiao Y, Ding R. “Iterative identification algorithm for Wiener nonlinear systems using the Newton method”. Applied Mathematical Modelling, 37(9), 6584-6591, 2013.
  • Lawryjnczuk M. “Modelling and predictive control of a neutralisation reactor using sparse support vector machine Wiener models”. Neurocomputing, 205(12), 311-328, 2016.
  • Tang Y, Han Z, Liu F, Guan X. “Identification and control of nonlinear system based on Laguerra-ELM Wiener model”. Communications in Nonlinear Science and Numerical Simulation, 38, 192-205, 2016.
  • Mahmoodi S, Poshtan J, Jahed-Motlagh MR, Montazeri A. “Nonlinear model predictive control of a pH neutralization process based on Wiener-Laguerre model”. Chemical Engineering Journal, 146(3), 328-337, 2009.
  • Chan LLT, Chen T, Chen J. “PID based nonlinear processes control model uncertainty improvement by using Gaussian process model”. Journal of Process Control, 42, 77-89, 2016.
  • Gomez JC, Jutan A, Baeyens E. “Wiener model identification and predictive control of a pH neutralisation process”. IEE Proceedings-Control Theory and Applications, 151(3), 329-338, 2004.
  • Ljung L. System Identification, New Jersey, USA, Prentice Hall, 1999.
  • Gerksic S, Juricic D, Strmcnik S, Matko D. “Wiener model based nonlinear predictive control”. International Journal of Systems Science, 31, 189-202, 2010.
  • Zhou L, Li X, Pan F. “Gradient based iterative parameter identification for Wiener nonlinear systems”. Applied Mathematical Modelling, 37, 8203-8209, 2013.
  • Vatankhah B, Farrokhi M. “Nonlinear model-predictive control with disturbance rejection property using adaptive neural networks”. Journal of the Franklin Institute, 354, 5201-5220, 2017.
  • Xie S, Xie Y, Gui W, Yang C. “Weighted-coupling CSTR modeling and model predictive control with parameter adaptive correction for the goethite process”. Journal of Process Control, 68, 254-267, 2018.

Gradient Based Identification of Wiener Systems

Yıl 2018, Cilt: 24 Sayı: 8, 1418 - 1424, 29.12.2018

Öz

In
systems consisting of a single block, the establishment of feedback control and
input-output relationship is common in classical control. However, considering
natural systems, there are many block structures and non-linear functions
within these blocks. The subject of this study is the identification of Wiener
type systems, which is the system structure in which the input signal is
applied to the linear block and its output is taken from the nonlinear
function. It is possible to use different control algorithms with
identification in this type of system where state feedback control is not
possible. In system identification, the mathematical relationship between the
input and output of the system has been obtained by the choice of auto
regressive with exogenous inputs (ARX)-polynomial cascade connection with using
the least squares method and gradient information. Three different benchmark
systems have been studied in MATLAB/Simulink and the performances of the
identifications made with the dataset are presented graphically.

Kaynakça

  • Jafari M, Salimifard M, Dehghani M. “Identification of multivariable nonlinear systems in the presence of colored noises using iterative hierarchical least squares Algorithm”. ISA Transactions, 53 (4), 1243-1252, 2014.
  • Salhi H, Kamoun S. “A recursive parametric estimation algorithm of multivariable nonlinear systems described by Hammerstein mathematical models”. Applied Mathematical Modelling, 39(16), 4951-4962, 2015.
  • Chang F, Luus R. “A nonlinear method for identification using Hammerstein model”. IEEE Transactions on Automatic Control, 16(5), 464-468, 1971.
  • Figueroa L, Cousseau JE, Werner S, Laakso T. “Adaptive control of a Wiener type system: application of a pH neutralization reactor”. Internaional Journal of Control, 80(2), 231-240, 2007.
  • Chi Q, Fei Z, Liu K, Liang J. “Latent-variable nonlinear model predictive control strategy for a pH neutralization process”. Asian Journal of Control, 17(6), 2427-2434, 2015.
  • Guo J, Yin LY, Zhao Y, Zhang JF. “Identification of Wiener systems with quantized inputs and binary-valued output observations”. Automatica, 78, 280-286, 2017.
  • Kibangou AY, Favier G. “Wiener-Hammerstein systems modeling using diagonal Volterra kernels coefficients”. IEEE Signal Processing Letter, 13(6), 381-384, 2006.
  • Lawrynjczuk M. “Nonlinear predictive control for Hammerstein-Wiener systems”. ISA Transactions, 55, 49-62, 2015.
  • Kazemi M, Arefi MM. “A fast iterative recursive least squares algorithm for Wiener model identification of highly nonlinear systems”. ISA Transactions, 67, 382-388, 2017.
  • Kalafatis A, Arifin N, Wang L, Cluett W. “A new approach to the identification of pH processes based on the Wiener model”. Chemical Engineering Science, 50(23), 3693-3701, 1995.
  • Zhang O, Ma Z, Li G, Qian Z, Guo X., “Temperature-dependent demagnetization nonlinear Wiener model with neural network for PM synchronous machines in electric vehicle”. 19th International Conference on Electrical Machines and Systems, Chiba, Japan, 13-16 November 2016.
  • Norquay SJ, Palazoglu A, Romagnoli JA. “Model predictive control based on Wiener models”. Chemical Engineering Science, 53(1), 75-84, 1998.
  • Kazemi M, Arefi MM. “Nonlinear generalized minimum variance control and control performance assessment of nonlinear systems based on a Wiener model”. Transactions of the Institute of Measurement and Control, https://doi.org/10.1177/0142331216685395.
  • Li S, Li Y. “Model predictive control of an intensified continuous reactor using a neural network Wiener model”. Neurocomputing, 185, 93-104, 2016.
  • Yu W, Pineda FJ. “Chemical process modelling with multiple neural networks”. European Control Conference, Porto, Portugal, 4-7 September 2001.
  • Zeydan M. “Bir petrol rafinerisi (TÜPRAŞ) akışkan yataklı katalitik parçalama ünitersinin (FCCU) bulanık modelleme uygulamaı”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 10(1), 101-110, 2002.
  • Ding F, Liu X, Liu M. “The recursive least squares identification algorithm for a class of Wiener nonlinear systems”. Journal of The Franklin Institute, 353(7), 1518-1526, 2016.
  • Liu M, Xiao Y, Ding R. “Iterative identification algorithm for Wiener nonlinear systems using the Newton method”. Applied Mathematical Modelling, 37(9), 6584-6591, 2013.
  • Lawryjnczuk M. “Modelling and predictive control of a neutralisation reactor using sparse support vector machine Wiener models”. Neurocomputing, 205(12), 311-328, 2016.
  • Tang Y, Han Z, Liu F, Guan X. “Identification and control of nonlinear system based on Laguerra-ELM Wiener model”. Communications in Nonlinear Science and Numerical Simulation, 38, 192-205, 2016.
  • Mahmoodi S, Poshtan J, Jahed-Motlagh MR, Montazeri A. “Nonlinear model predictive control of a pH neutralization process based on Wiener-Laguerre model”. Chemical Engineering Journal, 146(3), 328-337, 2009.
  • Chan LLT, Chen T, Chen J. “PID based nonlinear processes control model uncertainty improvement by using Gaussian process model”. Journal of Process Control, 42, 77-89, 2016.
  • Gomez JC, Jutan A, Baeyens E. “Wiener model identification and predictive control of a pH neutralisation process”. IEE Proceedings-Control Theory and Applications, 151(3), 329-338, 2004.
  • Ljung L. System Identification, New Jersey, USA, Prentice Hall, 1999.
  • Gerksic S, Juricic D, Strmcnik S, Matko D. “Wiener model based nonlinear predictive control”. International Journal of Systems Science, 31, 189-202, 2010.
  • Zhou L, Li X, Pan F. “Gradient based iterative parameter identification for Wiener nonlinear systems”. Applied Mathematical Modelling, 37, 8203-8209, 2013.
  • Vatankhah B, Farrokhi M. “Nonlinear model-predictive control with disturbance rejection property using adaptive neural networks”. Journal of the Franklin Institute, 354, 5201-5220, 2017.
  • Xie S, Xie Y, Gui W, Yang C. “Weighted-coupling CSTR modeling and model predictive control with parameter adaptive correction for the goethite process”. Journal of Process Control, 68, 254-267, 2018.
Toplam 28 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Makale
Yazarlar

İbrahim Alışkan 0000-0003-3901-4955

Yayımlanma Tarihi 29 Aralık 2018
Yayımlandığı Sayı Yıl 2018 Cilt: 24 Sayı: 8

Kaynak Göster

APA Alışkan, İ. (2018). Wiener Sistemlerinin Gradyan Tabanlı Tanımlaması. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 24(8), 1418-1424.
AMA Alışkan İ. Wiener Sistemlerinin Gradyan Tabanlı Tanımlaması. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. Aralık 2018;24(8):1418-1424.
Chicago Alışkan, İbrahim. “Wiener Sistemlerinin Gradyan Tabanlı Tanımlaması”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 24, sy. 8 (Aralık 2018): 1418-24.
EndNote Alışkan İ (01 Aralık 2018) Wiener Sistemlerinin Gradyan Tabanlı Tanımlaması. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 24 8 1418–1424.
IEEE İ. Alışkan, “Wiener Sistemlerinin Gradyan Tabanlı Tanımlaması”, Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, c. 24, sy. 8, ss. 1418–1424, 2018.
ISNAD Alışkan, İbrahim. “Wiener Sistemlerinin Gradyan Tabanlı Tanımlaması”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 24/8 (Aralık 2018), 1418-1424.
JAMA Alışkan İ. Wiener Sistemlerinin Gradyan Tabanlı Tanımlaması. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. 2018;24:1418–1424.
MLA Alışkan, İbrahim. “Wiener Sistemlerinin Gradyan Tabanlı Tanımlaması”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, c. 24, sy. 8, 2018, ss. 1418-24.
Vancouver Alışkan İ. Wiener Sistemlerinin Gradyan Tabanlı Tanımlaması. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. 2018;24(8):1418-24.





Creative Commons Lisansı
Bu dergi Creative Commons Al 4.0 Uluslararası Lisansı ile lisanslanmıştır.