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Uyarlamalı genişletilmiş bulanık fonksiyon durum gözetleyici temelli bilinmeyen yönlü kontrol

Yıl 2017, Cilt: 23 Sayı: 5, 519 - 526, 20.10.2017

Selami Beyhan

Öz

Bu
çalışmada, uyarlamalı genişletilmiş bulanık fonksyion durum gözetleyici temelli
denetleyici, doğrusal olmayan bilinmeyen ve belirsiz sistemlerin kontrolü için
önerilmiştir. Nussbaum-Kazanç tekniği kullanarak bilinmeyen kontrol işareti
yönündeki tekil durum engellenerek denetleyicinin serbestlik derecesi
artırılmıştır. Uyarlamalı genişletilmiş bulanık fonksiyon ile bilinmeyen sistem
dinamikleri yaklaşıklanmakta ve ölçülemeyen durumlar gözetlenmektedir. Kapalı
çevrim kontrol sistemindeki sinyallerin sınırlılığı Lyapunov kararlılık kriteri
ve Nussbaum fonksiyon özellikleri ile gösterilmiştir. Önerilen ve literatürde
bilinen bulanık sistem temelli denetleyiciler ters sarkaç sistemine benzetim ortamında,
esnek bağlantılı robot koluna ise gerçek zamanlı olarak uygulanmıştır. İzleme
hatası için mutlak hata toplamı (IAE), karesel hatanın toplamı (IAE) ve gerekli
kontrol işaretinin toplamı (IAU) performansları kullanarak tasarlanan
denetleyiciler karşılaştırılmıştır. Çalışmanın amacı sadece izleme
performansını artırmak değil, uyarlamalı genişletilmiş bulanık fonksiyon
gözetleyici temelli denetleyiciyi gerçek zamanlı sisteme uygulamak ve
bilinmeyen kontrol işareti yönünde denetlemeyi sağlamaktır.

Anahtar Kelimeler

Genişletilmiş bulanık fonksiyon Uyarlamalı gözetleyici temelli kontrol Nussbaum-kazanç tekniği Ters sarkaç Esnek bağlantılı robot kolu

Kaynakça

  • Landau ID, Rey D, Karimi A, Voda A, Franco A. “A flexible transmission system as a benchmark for robust digital control”. European Journal of Control, 1(2), 77-96, 1995.
  • Moberg S, Öhr J, Gunnarsson S. “A benchmark problem for robust control of a multivariable nonlinear exible manipulator”. 17th IFAC World Congress, Seoul, South Korea, 6-11 July 2008.
  • Quanser Inc. Rotary Flexible Joint User Manual, 2012.
  • Jayawardene TSS, Nakamura M, Goto S. “Accurate control position of belt drives under acceleration and velocity constraints”. International Journal of Control, Automation, and Systems, 1(3), 474-483, 2003.
  • Kune-Shiang T, Jian-Shiang C. “Toward the iterative learning control for belt-driven system using wavelet transformation”. Journal of Sound and Vibration, 286(4-5), 781-798, 2005.
  • ConsoliniL, Gerelli O, Guarino Lo Bianco C, Piazzi A. “Flexible joints control: A minimum-time feed-forward technique”. Mechatronics, 19(3), 348-356, 2009.
  • Talole SE, Kolhe JP, Phadke SB. “Extended-state-observer-based control of exible-joint system with experimental validation”. IEEE Transactions on Industrial Electronics, 57(4), 1411-1419, 2010.
  • Wang LX, Mendel JM. “Fuzzy basis functions, universal approximation, and orthogonal least-squares learning”. IEEE Transactions on Neural Networks, 3(5), 807-814, 1992.
  • Park JH, Park GT. “Robust adaptive fuzzy controller for non-a_ne nonlinear systems with dynamic rule activation”. International Journal of Robust and Nonlinear Control, 13(2), 117-139, 2003.
  • Shaocheng T, Shuai S, Yongming L. “Adaptive fuzzy decentralized control for stochastic large-scale nonlinear systems with unknown dead-zone and unmodeled dynamics”. Neurocomputing, 135, 367-377, 2014.
  • Boulkroune A, Bounar N, M'Saad M, Farza M. “Indirect adaptive fuzzy control scheme based on observer for nonlinear systems: A novel SPR-filter approach”. Neurocomputing, 135, 378-387, 2014.
  • Young HK., FL. Lewis, and CT. Abdallah, “A dynamic recurrent neural-network-based adaptive observer for a class of nonlinear systems”. Automatica, 33(8), 1539-1543, 1997.
  • Park JH, Yoon PS, Park GT. “Robust adaptive observer using fuzzy systems for uncertain nonlinear systems”. 10th IEEE International Conference on Fuzzy Systems, 2-5 December 2001.
  • Ionnou PA, Sun J. Robust Adaptive Control. Englewood Clifs, New Jersey, USA, Prentice-Hall, 1996.
  • Yih-Guang L, Tsu-Tian L, Wei-Yen W. “Observer-based adaptive fuzzy-neural control for unknown nonlinear dynamical systems”. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 29(5), 583-591, 1999.
  • Shaocheng T, Han-Xiong L, Wei W. “Observer-based adaptive fuzzy control for SISO nonlinear systems”. Fuzzy Sets and Systems, 148(3), 355-376, 2004.
  • Park JH, Seo SJ, Park GT. “Robust adaptive fuzzy controller for nonlinear system using estimation of bounds for approximation errors”. Fuzzy Sets and Systems, 133(1), 19-36, 2003.
  • Jang-Hyun P, Gwi-Tae P, Seong-Hwan K, Chae-Joo M. “Output-feedback control of uncertain nonlinear systems using a self-structuring adaptive fuzzy observer”. Fuzzy Sets and Systems, 151(1), 21-42, 2005.
  • Chung-Chun K, Ti-Hung C. “Observer-based indirect adaptive fuzzy sliding mode control with state variable Filters for unknown nonlinear dynamical systems”. Fuzzy Sets and Systems, 155(2), 292-308, 2005.
  • Boulkroune A, M. Tadjine, M. M'Saad, and M. Farza. “How to design a fuzzy adaptive controller based on observers for uncertain a_ne nonlinear systems”. Fuzzy Sets and Systems, 159(8), 926-948, 2008.
  • Qi R.., Mietek AB. “Stable indirect adaptive control based on discrete-time TS fuzzy model”. Fuzzy Sets and Systems, 159(8), 900-925, 2008.
  • Nussbaum RD. “Some remarks on the conjecture on the parameter adaptive control”. Systems and Control Letters, 3, 243-246, 1983.
  • Ge SS, Hong F, Lee TH. “Adaptive neural control of nonlinear time-delay systems with unknown virtual control coefficients”. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 34(1), 499-516, 2004.
  • Zhaoxu Y, Shugang L, Hongbin D. “Razumikhin-nussbaum-lemma-based adaptive neural control for uncertain stochastic pure-feedback nonlinear systems with time-varying delays”. International Journal of Robust and Nonlinear Control, 23(11), 1214-1239, 2013.
  • Wang T., S. Tong, and Y. Li. “Robust adaptive fuzzy output feedback control for stochastic nonlinear systems with unknown control direction”. Neurocomputing, 106, 31-41, 2013.
  • Türkşen IB. “Fuzzy functions with LSE”. Applied Soft Computing, 8(3), 1178-1188, 2008.
  • Beyhan S, Alcı M. “Fuzzy functions based ARX model and new fuzzy basis function models for nonlinear system identi_cation”. Applied Soft Computing, 10(2), 439-444, 2010.
  • Beyhan S, Alcı M. “Extended fuzzy function model with stable learning methods for online system identification”. International Journal of Adaptive Control and Signal Processing, 25(2), 168-182, 2011.
  • Fazel Z. M. H., Zarinbal M, N. Ghanbari, I.B. Turksen. “A new fuzzy functions model tuned by hybridizing imperialist competitive algorithm and simulated annealing. Application: Stock price prediction”. Information Sciences, 222, 213-228, 2013.
  • Alcı M., S. Beyhan, “Fuzzy Functions with function expansion model for nonlinear system identification”. International Journal of Intelligent Automation & Soft Computing, 23(1), 87-94, 2017.
  • Çelikyılmaz A., IB. Türkşen, R. Aktaş, M. M. Doğanay, N. B. Ceylan, “A new classifier design with fuzzy functions”. In Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, volume 4482 of Lecture Notes in Computer Science, pages 136-143. Springer Berlin/Heidelberg, 2007.
  • Çelikyılmaz A, Türkşen IB, Aktaş R, Doganay MM, Ceylan NB. “Increasing accuracy of two-class pattern recognition with enhanced fuzzy functions”. Expert Systems with Applications, 36(2), 1337-1354, 2009.
  • Türkşen IB., Çelikyılmaz A. “Comparison of fuzzy functions with fuzzy rule base approaches”. International Journal of Fuzzy Systems, 8(3), 137-149, 2006.
  • Türkşen IB. “Review of fuzzy system models with an emphasis on fuzzy functions”. Transactions of the Institute of Measurement and Control, 31(1), 7-31, 2009.
  • Zarandi MHF., M. Zarinbal, A. Zarinbal, IB. Turksen., M. Izadi, “Using type-2 fuzzy function for diagnosing brain tumors based on image processing approach”. International Conference on In Fuzzy Systems, Barcelona, Spain, 18-23 July 2010.
  • Yong-Tae K., Z. Z. Bien, “Robust adaptive fuzzy control in the presence of external disturbance and approximation error”. Fuzzy Sets and Systems, 148(3), 377-393, 2004.

An adaptive extended fuzzy function state-observer based control with unknown control direction

Yıl 2017, Cilt: 23 Sayı: 5, 519 - 526, 20.10.2017

Selami Beyhan

Öz

In
this paper, a novel adaptive extended fuzzy function state observer-based
controller is proposed to control a class of unknown or uncertain nonlinear
systems. The controller uses Nussbaum-gain technique from literature to prevent
controller singularity with unknown control direction and the controller degree
of freedom is increased. A state observer which employs the adaptive extended
fuzzy function system to approximate a nonlinear system dynamics and estimates
the unmeasurable state. The stability of closed-loop control system are shown
using Lyapunov stability criterion and Nussbaum function property. The proposed
and conventional fuzzy system based controllers are designed to control an
inverted pendulum in simulation and a flexible-joint manipulator in real-time
experiment. The integral of absoulte error (IAE) of tracking, integral of
squared error (ISE) of tracking and integral of required absolute control
signal (IAU) performances are compared in applications. The aim of the paper is
not only to improve the tracking performances, but also to implement the
adaptive extended fuzzy function based controller to a real-time system and
conduct the tracking with unknown control direction.

Anahtar Kelimeler

Extended fuzzy functions Adaptive observer-based control Nussbaum gain technique Inverted pendulum Flexible-joint manipulator

Kaynakça

  • Landau ID, Rey D, Karimi A, Voda A, Franco A. “A flexible transmission system as a benchmark for robust digital control”. European Journal of Control, 1(2), 77-96, 1995.
  • Moberg S, Öhr J, Gunnarsson S. “A benchmark problem for robust control of a multivariable nonlinear exible manipulator”. 17th IFAC World Congress, Seoul, South Korea, 6-11 July 2008.
  • Quanser Inc. Rotary Flexible Joint User Manual, 2012.
  • Jayawardene TSS, Nakamura M, Goto S. “Accurate control position of belt drives under acceleration and velocity constraints”. International Journal of Control, Automation, and Systems, 1(3), 474-483, 2003.
  • Kune-Shiang T, Jian-Shiang C. “Toward the iterative learning control for belt-driven system using wavelet transformation”. Journal of Sound and Vibration, 286(4-5), 781-798, 2005.
  • ConsoliniL, Gerelli O, Guarino Lo Bianco C, Piazzi A. “Flexible joints control: A minimum-time feed-forward technique”. Mechatronics, 19(3), 348-356, 2009.
  • Talole SE, Kolhe JP, Phadke SB. “Extended-state-observer-based control of exible-joint system with experimental validation”. IEEE Transactions on Industrial Electronics, 57(4), 1411-1419, 2010.
  • Wang LX, Mendel JM. “Fuzzy basis functions, universal approximation, and orthogonal least-squares learning”. IEEE Transactions on Neural Networks, 3(5), 807-814, 1992.
  • Park JH, Park GT. “Robust adaptive fuzzy controller for non-a_ne nonlinear systems with dynamic rule activation”. International Journal of Robust and Nonlinear Control, 13(2), 117-139, 2003.
  • Shaocheng T, Shuai S, Yongming L. “Adaptive fuzzy decentralized control for stochastic large-scale nonlinear systems with unknown dead-zone and unmodeled dynamics”. Neurocomputing, 135, 367-377, 2014.
  • Boulkroune A, Bounar N, M'Saad M, Farza M. “Indirect adaptive fuzzy control scheme based on observer for nonlinear systems: A novel SPR-filter approach”. Neurocomputing, 135, 378-387, 2014.
  • Young HK., FL. Lewis, and CT. Abdallah, “A dynamic recurrent neural-network-based adaptive observer for a class of nonlinear systems”. Automatica, 33(8), 1539-1543, 1997.
  • Park JH, Yoon PS, Park GT. “Robust adaptive observer using fuzzy systems for uncertain nonlinear systems”. 10th IEEE International Conference on Fuzzy Systems, 2-5 December 2001.
  • Ionnou PA, Sun J. Robust Adaptive Control. Englewood Clifs, New Jersey, USA, Prentice-Hall, 1996.
  • Yih-Guang L, Tsu-Tian L, Wei-Yen W. “Observer-based adaptive fuzzy-neural control for unknown nonlinear dynamical systems”. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 29(5), 583-591, 1999.
  • Shaocheng T, Han-Xiong L, Wei W. “Observer-based adaptive fuzzy control for SISO nonlinear systems”. Fuzzy Sets and Systems, 148(3), 355-376, 2004.
  • Park JH, Seo SJ, Park GT. “Robust adaptive fuzzy controller for nonlinear system using estimation of bounds for approximation errors”. Fuzzy Sets and Systems, 133(1), 19-36, 2003.
  • Jang-Hyun P, Gwi-Tae P, Seong-Hwan K, Chae-Joo M. “Output-feedback control of uncertain nonlinear systems using a self-structuring adaptive fuzzy observer”. Fuzzy Sets and Systems, 151(1), 21-42, 2005.
  • Chung-Chun K, Ti-Hung C. “Observer-based indirect adaptive fuzzy sliding mode control with state variable Filters for unknown nonlinear dynamical systems”. Fuzzy Sets and Systems, 155(2), 292-308, 2005.
  • Boulkroune A, M. Tadjine, M. M'Saad, and M. Farza. “How to design a fuzzy adaptive controller based on observers for uncertain a_ne nonlinear systems”. Fuzzy Sets and Systems, 159(8), 926-948, 2008.
  • Qi R.., Mietek AB. “Stable indirect adaptive control based on discrete-time TS fuzzy model”. Fuzzy Sets and Systems, 159(8), 900-925, 2008.
  • Nussbaum RD. “Some remarks on the conjecture on the parameter adaptive control”. Systems and Control Letters, 3, 243-246, 1983.
  • Ge SS, Hong F, Lee TH. “Adaptive neural control of nonlinear time-delay systems with unknown virtual control coefficients”. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 34(1), 499-516, 2004.
  • Zhaoxu Y, Shugang L, Hongbin D. “Razumikhin-nussbaum-lemma-based adaptive neural control for uncertain stochastic pure-feedback nonlinear systems with time-varying delays”. International Journal of Robust and Nonlinear Control, 23(11), 1214-1239, 2013.
  • Wang T., S. Tong, and Y. Li. “Robust adaptive fuzzy output feedback control for stochastic nonlinear systems with unknown control direction”. Neurocomputing, 106, 31-41, 2013.
  • Türkşen IB. “Fuzzy functions with LSE”. Applied Soft Computing, 8(3), 1178-1188, 2008.
  • Beyhan S, Alcı M. “Fuzzy functions based ARX model and new fuzzy basis function models for nonlinear system identi_cation”. Applied Soft Computing, 10(2), 439-444, 2010.
  • Beyhan S, Alcı M. “Extended fuzzy function model with stable learning methods for online system identification”. International Journal of Adaptive Control and Signal Processing, 25(2), 168-182, 2011.
  • Fazel Z. M. H., Zarinbal M, N. Ghanbari, I.B. Turksen. “A new fuzzy functions model tuned by hybridizing imperialist competitive algorithm and simulated annealing. Application: Stock price prediction”. Information Sciences, 222, 213-228, 2013.
  • Alcı M., S. Beyhan, “Fuzzy Functions with function expansion model for nonlinear system identification”. International Journal of Intelligent Automation & Soft Computing, 23(1), 87-94, 2017.
  • Çelikyılmaz A., IB. Türkşen, R. Aktaş, M. M. Doğanay, N. B. Ceylan, “A new classifier design with fuzzy functions”. In Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, volume 4482 of Lecture Notes in Computer Science, pages 136-143. Springer Berlin/Heidelberg, 2007.
  • Çelikyılmaz A, Türkşen IB, Aktaş R, Doganay MM, Ceylan NB. “Increasing accuracy of two-class pattern recognition with enhanced fuzzy functions”. Expert Systems with Applications, 36(2), 1337-1354, 2009.
  • Türkşen IB., Çelikyılmaz A. “Comparison of fuzzy functions with fuzzy rule base approaches”. International Journal of Fuzzy Systems, 8(3), 137-149, 2006.
  • Türkşen IB. “Review of fuzzy system models with an emphasis on fuzzy functions”. Transactions of the Institute of Measurement and Control, 31(1), 7-31, 2009.
  • Zarandi MHF., M. Zarinbal, A. Zarinbal, IB. Turksen., M. Izadi, “Using type-2 fuzzy function for diagnosing brain tumors based on image processing approach”. International Conference on In Fuzzy Systems, Barcelona, Spain, 18-23 July 2010.
  • Yong-Tae K., Z. Z. Bien, “Robust adaptive fuzzy control in the presence of external disturbance and approximation error”. Fuzzy Sets and Systems, 148(3), 377-393, 2004.
Toplam 36 adet kaynakça vardır.

Ayrıntılar

Konular Mühendislik
Bölüm Makale
Yazarlar

Selami Beyhan

Yayımlanma Tarihi 20 Ekim 2017
Yayımlandığı Sayı Yıl 2017 Cilt: 23 Sayı: 5

Kaynak Göster

APA Beyhan, S. (2017). Uyarlamalı genişletilmiş bulanık fonksiyon durum gözetleyici temelli bilinmeyen yönlü kontrol. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 23(5), 519-526.
AMA Beyhan S. Uyarlamalı genişletilmiş bulanık fonksiyon durum gözetleyici temelli bilinmeyen yönlü kontrol. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. Ekim 2017;23(5):519-526.
Chicago Beyhan, Selami. “Uyarlamalı genişletilmiş bulanık Fonksiyon Durum gözetleyici Temelli Bilinmeyen yönlü Kontrol”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 23, sy. 5 (Ekim 2017): 519-26.
EndNote Beyhan S (01 Ekim 2017) Uyarlamalı genişletilmiş bulanık fonksiyon durum gözetleyici temelli bilinmeyen yönlü kontrol. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 23 5 519–526.
IEEE S. Beyhan, “Uyarlamalı genişletilmiş bulanık fonksiyon durum gözetleyici temelli bilinmeyen yönlü kontrol”, Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, c. 23, sy. 5, ss. 519–526, 2017.
ISNAD Beyhan, Selami. “Uyarlamalı genişletilmiş bulanık Fonksiyon Durum gözetleyici Temelli Bilinmeyen yönlü Kontrol”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 23/5 (Ekim 2017), 519-526.
JAMA Beyhan S. Uyarlamalı genişletilmiş bulanık fonksiyon durum gözetleyici temelli bilinmeyen yönlü kontrol. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. 2017;23:519–526.
MLA Beyhan, Selami. “Uyarlamalı genişletilmiş bulanık Fonksiyon Durum gözetleyici Temelli Bilinmeyen yönlü Kontrol”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, c. 23, sy. 5, 2017, ss. 519-26.
Vancouver Beyhan S. Uyarlamalı genişletilmiş bulanık fonksiyon durum gözetleyici temelli bilinmeyen yönlü kontrol. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. 2017;23(5):519-26.





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