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

Öz

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.

Anahtar Kelimeler

• VTOL sistemi
• Yapay Sinir Ağları
• Uyarlanabilir PID kontrolör

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

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.

Keywords

• VTOL system
• Artificial Neural Networks
• Adaptive PID controller

Kaynakça

1. Aström, K. J., and Hägglund, T. (1995). PID controllers: theory, design, and tuning. Research Triangle Park, NC: Instrument society of America.
2. Chen, J., and Huang, T. C. (2004). Applying neural networks to on-line updated PID controllers for nonlinear process control. Journal of process control, 14(2), 211-230.
3. Clarke, D. W., and Gawthrop, P. J. (1975, September). Self-tuning controller. In Proceedings of the Institution of Electrical Engineers (Vol. 122, No. 9, pp. 929-934). IET Digital Library.
4. Dydek, Z. T., Annaswamy, A. M., and Lavretsky, E. (2012). Adaptive control of quadrotor UAVs: A design trade study with flight evaluations. IEEE Transactions on control systems technology, 21(4), 1400-1406.
5. Kumar, R., Srivastava, S., and Gupta, J. R. P. (2016, July). Artificial neural network based PID controller for online control of dynamical systems. In 2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES) (pp. 1-6). IEEE.
6. Kumar, V., Gaur, P., and Mittal, A. P. (2014). ANN based self tuned PID like adaptive controller design for high performance PMSM position control. Expert Systems with Applications, 41(17), 7995-8002.
7. Ma, H. J., Liu, Y., Li, T., and Yang, G. H. (2018). Nonlinear high-gain observer-based diagnosis and compensation for actuator and sensor faults in a quadrotor unmanned aerial vehicle. IEEE Transactions on Industrial Informatics, 15(1), 550-562.
8. Mosaad, M. I., and Salem, F. (2014). LFC based adaptive PID controller using ANN and ANFIS techniques. Journal of Electrical Systems and Information Technology, 1(3), 212-222.

9. Muruganandam, M., and Madheswaran, M. (2013). Stability analysis and implementation of chopper fed DC series motor with hybrid PID-ANN controller. International Journal of Control, Automation and Systems, 11(5), 966-975.
10. Nohooji, H. R. (2020). Constrained Neural Adaptive PID Control for Robot Manipulators. Journal of the Franklin Institute.
11. Ogata, K., and Yang, Y. (2010). Modern control engineering (Vol. 5). Upper Saddle River, NJ: Prentice hall
12. Quanser Inc. (2011) QNET VTOL Instructor Workbook, ftp://ftp.ni.com/evaluation/academic/ekits/QNET_VTOL_Workbook_Student.pdf.
13. Sahin, S., Işler, Y., and Güzeliş, C. (2016). Real-Time Simulation Platform for Controller Design, Test, and Redesign. In Real-Time Simulation Technologies: Principles, Methodologies, and Applications (pp. 482-521). CRC Press.
14. Skogestad, S. (2003). Simple analytic rules for model reduction and PID controller tuning. Journal of process control, 13(4), 291-309.
15. Taşören, A. E., Örenbaş, H., and Şahin, S. (2018, October). Analyze and Comparison of Different PID Tuning Methods on a Brushless DC Motor Using Atmega328 Based Microcontroller Unit. In 2018 6th International Conference on Control Engineering & Information Technology (CEIT) (pp. 1-4). IEEE.
16. Van Varseveld, Robert B., and Gary M. Bone. "Accurate position control of a pneumatic actuator using on/off solenoid valves." IEEE/ASME Transactions on mechatronics 2.3 (1997): 195-204.
17. Vega, P., Prada, C., and Aleixandre, V. (1991, May). Self-tuning predictive PID controller. In IEE Proceedings D (Control Theory and Applications) (Vol. 138, No. 3, pp. 303-312). IET Digital Library.
18. Wittenmark, B., and Åström, K. J. (1980). Simple self-tuning controllers. In Methods and Applications in Adaptive Control (pp. 21-30). Springer, Berlin, Heidelberg.
19. Yamamoto, T., and Shah, S. L. (2004). Design and experimental evaluation of a multivariable self-tuning PID controller. IEE Proceedings-Control Theory and Applications, 151(5), 645-652.
20. Yamamoto, T., Takao, K., and Yamada, T. (2008). Design of a data-driven PID controller. IEEE Transactions on Control Systems Technology, 17(1), 29-39.
21. Yu, X. H., Chen, G. A., and Cheng, S. X. (1995). Dynamic learning rate optimization of the backpropagation algorithm. IEEE Transactions on Neural Networks, 6(3), 669-677.