Öz
Gerçek
zamanlı sistemlerin modellenemeyen dinamikleri ve bozucu etkileri sistemin
doğru çalışmasını engellemektedir. Sistemin kontrolü için tasarlanan
denetleyiciler, istenmeyen etkileri dikkate alacak şekilde olmalıdır. Bu
çalışmada, doğrusal sistemler için uyarlamalı belirsizlik modelleyici temelli
model-öngörülü denetleyici (UMPC) önerilmiştir. Modelleyicide yapay sinir ağı
(YSA) yapısı kullanılarak belirsizlik fonksiyonunun uyarlamalı öğrenme adımı
ile hızlı şekilde yaklaşıklanması sağlanmıştır. Uyarlamalı belirsizlik
modelleyici temelli model-öngörülü denetleyicinin kararlılığı Lyapunov aday
fonksiyonu ile gösterilmiştir. Standart MPC ve önerilen UMPC gerçek-zamanlı
DC/DC güç dönüştürücü kontrolüne uygulanmıştır. Standart MPC kullanıldığında
bilinmeyen parametreler ve ortam gürültüsünden kaynaklı DC/DC dönüştürücü iyi
izleme sağlayamamıştır. Fakat önerilen yapının uygulanması ile belirsizlikler
tahmin edilerek ve etkisi sistem dinamiklerinde kullanılarak hassas ve başarılı
izleme sonuçları elde edilmiştir. Önerilen yapının sonraki çalışmalarda
kullanılması öngörülmektedir.
Anahtar Kelimeler
Model-öngörülü denetleyici Uyarlamalı doğrusal model-öngörülü denetleyici Uyarlamalı YSA Kararlılık Gerçek zamanlı DC/DC güç dönüştürücü
Kaynakça
- Adetola V, Guay M. “Robust adaptive mpc for constrained uncertain nonlinear systems”. International Journal of Adaptive Control and Signal Processing, 25(2), 155-167, 2011.
- Le Maître OP, Knio OM. Introduction: Uncertainty Quantification and Propagation, Amsterdam, Netherlands Springer, 2010.
- Yao B, Tomizuka M. “Adaptive robust control of SISO nonlinear systems in a semi-strict feedback form”. Automatica, 33(5), 893-900, 1997.
- Qin SJ, Badgwell TA. “A survey of industrial model predictive control technology”. Control engineering practice, 11(7), 733-764, 2003.
- Rawlings JB. “Tutorial overview of model predictive control”. Control Systems, 20(3), 38-52, 2000.
- Yan Z, Wang J. “Model predictive control of nonlinear systems with unmodeled dynamics based on feedforward and recurrent neural networks”. IEEE Transactions on Industrial Informatics, 8(4), 746-756, 2012.
- Teixeira M, Żak SH. “Stabilizing controller design for uncertain nonlinear systems using fuzzy models”. IEEE Transactions on Fuzzy Systems, 7(2), 133-142, 1999.
- Calise AJ, Hovakimyan N, Idan M. “Adaptive output feedback control of nonlinear systems using neural networks”. Automatica, 37(8), 1201-1211, 2001.
- Rojo-Álvarez JL, Martínez-Ramón M, Prado-Cumplido D, Artés-Rodríguez A, Figueiras-Vidal AR. “Support vector method for robust ARMA system identification”. IEEE Transactions on Signal Processing, 52(1), 155-164, 2004
- Jang JSR, Sun CT, Mizutani E. Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. Upper Saddle River, USA, Pearson, 1997.
- Nauck D, Kruse R. “Neuro-fuzzy systems for function approximation”. Fuzzy sets and systems, 101(2), 261-271, 1999.
- Klančar G, Škrjanc I. “Tracking-error model-based predictive control for mobile robots in real time”. Robotics and Autonomous Systems, 55(6), 460-469, 2007.
- Mohammadkhani MA, Bayat F, Jalali AA. (2014). “Design of explicit model predictive control for constrained linear systems with disturbances”. International Journal of Control, Automation and Systems, 12(2), 294-301, 2014.
- Zeltom Real-Time Hardware-in-the-loop Control Platform for Matlab/Simulink. “HILINK Real-Time Control Platform for MATLAB/Simulink”
Öz
The
effect of the unmodeled dynamics and unknown disturbances prevent the accurate
control of the real-time systems. The designed controllers must undertake the
effect of these undesired uncertainties. In this paper, adaptive uncertainty
modeling based model predictive controller is proposed for the control of
uncertain linear systems. The uncertainty modeling structure uses an artificial
neural network with adaptive learning rate for fast approximation. The
stability of the proposed adaptive uncertainty modeling based model predictive
control (UMPC) is shown using Lyapunov candidate function. Conventional MPC and
proposed UMPC are applied to the control of a real-time DC/DC buck power converter.
The conventional MPC cannot accurately control the DC/DC converter due to the
unknown parameters and unmodeled dynamics. However, the proposed UMPC
controller can accurately control the system with modeling the uncertainties in
controller dynamics. The proposed controller is promising to control uncertain
systems in future applications.
Anahtar Kelimeler
Model predictive control Adaptive linear model predictive control Adaptive neural networks Stability Real-time DC/DC converter
Kaynakça
- Adetola V, Guay M. “Robust adaptive mpc for constrained uncertain nonlinear systems”. International Journal of Adaptive Control and Signal Processing, 25(2), 155-167, 2011.
- Le Maître OP, Knio OM. Introduction: Uncertainty Quantification and Propagation, Amsterdam, Netherlands Springer, 2010.
- Yao B, Tomizuka M. “Adaptive robust control of SISO nonlinear systems in a semi-strict feedback form”. Automatica, 33(5), 893-900, 1997.
- Qin SJ, Badgwell TA. “A survey of industrial model predictive control technology”. Control engineering practice, 11(7), 733-764, 2003.
- Rawlings JB. “Tutorial overview of model predictive control”. Control Systems, 20(3), 38-52, 2000.
- Yan Z, Wang J. “Model predictive control of nonlinear systems with unmodeled dynamics based on feedforward and recurrent neural networks”. IEEE Transactions on Industrial Informatics, 8(4), 746-756, 2012.
- Teixeira M, Żak SH. “Stabilizing controller design for uncertain nonlinear systems using fuzzy models”. IEEE Transactions on Fuzzy Systems, 7(2), 133-142, 1999.
- Calise AJ, Hovakimyan N, Idan M. “Adaptive output feedback control of nonlinear systems using neural networks”. Automatica, 37(8), 1201-1211, 2001.
- Rojo-Álvarez JL, Martínez-Ramón M, Prado-Cumplido D, Artés-Rodríguez A, Figueiras-Vidal AR. “Support vector method for robust ARMA system identification”. IEEE Transactions on Signal Processing, 52(1), 155-164, 2004
- Jang JSR, Sun CT, Mizutani E. Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. Upper Saddle River, USA, Pearson, 1997.
- Nauck D, Kruse R. “Neuro-fuzzy systems for function approximation”. Fuzzy sets and systems, 101(2), 261-271, 1999.
- Klančar G, Škrjanc I. “Tracking-error model-based predictive control for mobile robots in real time”. Robotics and Autonomous Systems, 55(6), 460-469, 2007.
- Mohammadkhani MA, Bayat F, Jalali AA. (2014). “Design of explicit model predictive control for constrained linear systems with disturbances”. International Journal of Control, Automation and Systems, 12(2), 294-301, 2014.
- Zeltom Real-Time Hardware-in-the-loop Control Platform for Matlab/Simulink. “HILINK Real-Time Control Platform for MATLAB/Simulink”
Ayrıntılar
| Konular | Mühendislik |
|---|---|
| Bölüm | Makale |
| Yazarlar | |
| Yayımlanma Tarihi | 27 Aralık 2016 |
| Yayımlandığı Sayı | Yıl 2016 Cilt: 22 Sayı: 8 |
