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Journals / Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi / 2016 / Cilt: 22 - Sayı: 8

Artificial neural network based adaptive linear model predictive control

Yapay sinir ağı temelli uyarlamalı doğrusal model-öngörülü kontrol

Meriç Çetin
Selami Beyhan
Bedri Bahtiyar
Pages
650–658
Publication Date
2016-12-27
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The proposed controller is promising to control uncertain systems in future applications.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The effect of the unmodeled dynamics and unknown disturbances prevent the accurate control of the real-time systems.
📊
Key Findings & Data:The uncertainty modeling structure uses an artificial neural network with adaptive learning rate for fast approximation.
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Conclusion & Impact:The proposed controller is promising to control uncertain systems in future applications.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Model-öngörülü denetleyici · Uyarlamalı doğrusal model-öngörülü denetleyici · Uyarlamalı YSA

Abstract

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.

Özet

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.

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Cite This Article

Çetin, M., Beyhan, S., Bahtiyar, B. (2016). Artificial neural network based adaptive linear model predictive control. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, Cilt: 22 - Sayı: 8, 650–658. https://ajindex.com/articles/42569

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