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Artificial Neural Network-Based Adaptive PID Controller Design for Vertical Takeoff and Landing Model

Dikey Kalkış ve İniş Sistemi Modeli için Yapay Sinir Ağı Tabanlı Uyarlanır PID Kontrolör Tasarımı

Ali Egemen Taşören(IZMIR KATIP CELEBI UNIVERSITY)0000-0001-8711-2010
Alkım Gökçen(IZMIR KATIP CELEBI UNIVERSITY)0000-0002-8131-388X
Mehmet Uğur Soydemir(IZMIR KATIP CELEBI UNIVERSITY)0000-0002-2327-1642
Savaş Şahin(IZMIR KATIP CELEBI UNIVERSITY)0000-0003-2065-6907
Pages
87–93
Publication Date
2020-08-15
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AI Summary & Key Takeaways

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

The obtained results are compared to the conventional PID controller whose parameters are tuned by Simulink PID tuner application in terms of mean square error, integral absolute error, the settling time, and the percentage overshoot.

📌 3 Key Takeaways

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Objective & Hypothesis:This study presents an artificial neural network (ANN) based adaptive proportional integral derivative (PID) controller algorithm which is developed to control the pitch angle of the vertical takeoff and landing (VTOL) system model.
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Key Findings & Data:They provide constant values for the conventional PID controller parameters for a closed-loop system however it is obvious that the unsatisfied tracking error performances in the closed-loop system might be addressed as a problem to be optimized.
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Conclusion & Impact:The obtained results are compared to the conventional PID controller whose parameters are tuned by Simulink PID tuner application in terms of mean square error, integral absolute error, the settling time, and the percentage overshoot.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:VTOL sistemi · Yapay Sinir Ağları · Uyarlanabilir PID kontrolör

Abstract

This study presents an artificial neural network (ANN) based adaptive proportional integral derivative (PID) controller algorithm which is developed to control the pitch angle of the vertical takeoff and landing (VTOL) system model. To find appropriate conventional PID controller parameters, either step response or vibration method might be used in a way of the widely used approach. They provide constant values for the conventional PID controller parameters for a closed-loop system however it is obvious that the unsatisfied tracking error performances in the closed-loop system might be addressed as a problem to be optimized. To overcome this problem, the parameters of the proposed adaptive PID controller might be determined with an ANN constructed feedforward multilayer perceptron. The proposed controller algorithm possesses the gradient descent with momentum update rule for the adaptiveness of the obtained PID parameters. The proposed adaptive PID controller algorithm is tested for the pitch angle of the VTOL system model in the MATLAB/Simulink environment in terms of the sinusoidal and step signals as the desired outputs. The obtained results are compared to the conventional PID controller whose parameters are tuned by Simulink PID tuner application in terms of mean square error, integral absolute error, the settling time, and the percentage overshoot.

Özet

Çalışmada dikey kalkış ve iniş (VTOL) sistem modelinin eğim açısını kontrol etmek için geliştirilen yapay sinir ağı (YSA) tabanlı uyarlanabilir oransal integral türev (PID) denetleyici algoritması tasarlanmıştır. Uygun geleneksel PID denetleyici parametrelerinin hesaplanılmasında, basamak yanıtı ve titreşim metodu yaygın olarak kullanılan tekniklerdir. Bu yöntemler kapalı döngü sistemi içinde geleneksel PID kontrolör için sabit parametreler bulurlar, ancak kapalı döngü sistemindeki yetersiz izleme hatası performansının en iyi hale getirilmesi mevcut bir problem olarak gösterilebilir. Bu sorunun üstesinden gelmek için, önerilen uyarlanabilir PID denetleyicinin parametreleri bir ileri beslemeli çok katmanlı algılayıcı YSA yardımı ile sağlanabilir. Önerilen kontrolör algoritması, elde edilen uyarlanabilir PID denetleyici parametrelerinin uyarlanabilirliği için devinirlik güncellemeli gradyan inişi yöntemini kullanmaktadır. Önerilen uyarlanabilir PID denetleyici algoritması, MATLAB / Simulink ortamında VTOL sistem modelinin yunuslama açısı için arzu edilen çıkışlar sinüs ve birim basamak sinyalleri anlamında test edilmiştir. Elde edilen sonuçlar, parametreleri Simulink PID ayarlayıcı uygulaması ile hesaplanmış geleneksel PID denetleyici ile ortalama kare hata, integral mutlak hata, oturma süresi ve aşım yüzdesi açısından karşılaştırılır.

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Cite This Article

Taşören, A. E., Gökçen, A., Soydemir, M. U., Şahin, S. (2020). Artificial Neural Network-Based Adaptive PID Controller Design for Vertical Takeoff and Landing Model. Avrupa Bilim ve Teknoloji Dergisi, Özel Sayı, 87–93. https://doi.org/10.31590/ejosat.779085

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