Sabit kanatlı bir İHA’nın model öngörülü kontrolü için döngüde yazılım simülasyonu

Yıl 2024, , 318 - 324, 15.01.2024

Zülfü Kuzu , Fatma Yıldız Tascıkaraoglu

https://doi.org/10.28948/ngumuh.1331207

Öz

Bu çalışmada, bilgisayar tabanlı bir uçuş simülatörü olan X-Plane ile Simulink ortak çalışması yapılarak bir Cessna 172 uçağının rüzgârlı hava koşulları altında hız kontrolü gerçekleştirilmiştir. Trim koşullarında doğrusallaştırılan model için model öngörülü kontrolcü (MPC) geliştirilmiş ve döngüde yazılım (SIL) testleri yapılmıştır. Modele, rüzgâr bozucu etkisi olarak literatürde çok tercih edilen Von Karman rüzgâr türbülans modeli eklenmiştir. Veri iletişimi, gerçek zamanlı bir kullanıcı olan datagram protokolü (UDP) ile sağlanmıştır. Benzetim çalışmalarının sonuçları, literatürdeki PID kontrol tabanlı otopilot geliştirme çalışmalarının sonuçlarıyla karşılaştırılarak incelenmiştir. Buna göre, uçuş kontrol yüzeyleri ve gaz kolu kısıtlarının da dikkate alındığı MPC yöntemiyle kontrol edilen İHA’da, yunuslama stabilitesi korunarak hız referansı değişiklikleri doğru bir şekilde izlenmiştir ve benzeri çalışmalara kıyasla daha başarılı sanal uçuş testleri gerçekleştirildiği görülmüştür.

Anahtar Kelimeler

Model Öngörülü Kontrol, Döngüde yazılım simülasyon, X-Plane, İHA

Software-in-the-loop simulation of model predictive control applied to a fixed-wing UAV

Öz

In this study, the speed control of a Cessna 172 aircraft is carried out under windy weather conditions by combining X-Plane, which is a computer-based flight simulator, with Simulink. Model predictive controller (MPC) is developed for the model linearized under trim conditions and software-in-the-loop (SIL) tests are performed. Von Karman wind turbulence model, which is widely preferred in the literature as a wind disturbance effect, is added to the model. Data communication is provided through a real-time user datagram protocol (UDP). The results of simulation studies are examined by comparing those of PID control-based autopilot development studies in the literature. Accordingly, in the UAV controlled by the MPC method, which also takes into account flight control surfaces and throttle constraints, the speed reference changes are accurately monitored while maintaining pitch stability, and it is observed that more successful virtual flight tests are carried out compared to the similar studies.

Anahtar Kelimeler

Model Predictive Control, Software-in-the-loop, X-Plane, UAV

Kaynakça

• A. Kaviyarasu, A. Saravanakumar and M. Logavenkatesh, Software in Loop Simulation based Waypoint Navigation for Fixed Wing UAV, Defence Science Journal, 71 (4), pp. 448-455, 2021. https:// doi.org/10.14429/dsj.71.16164
• M. A. Tahir, I. Mir and T. U. Islam, A review of UAV platforms for autonomous applications: comprehensive analysis and future directions, IEEE Access, 11, pp. 52540-52554, 2023. doi:10.1109/ACCESS.2023.3273 780.
• L. Yu, G. He, S. Zhao, X. Wang and L. Shen, Design and Implementation of a Hardware-in-the-Loop Simulation System for a Tilt Trirotor UAV, Journal of AdvancedTransportation, 2020. https://doi.org/10.11 55/2020/4305742
• K. P. Valavanis and G. J. Vachtsevanos Introduction Handbook of Unmanned Aerial Vehicles 2015.
• D. Sartori, F. Quagliotti, M. J. Rutherford and K.P. Valavanis, Design and development of a backstepping controller autopilot for fixed-wing UAVs, The Aeronautical Journal, 125 (1294), pp. 2087-2113, 2021. doi:10.1017/aer.2021.53
• M. G. Michailidis, M. J. Rutherford and Kç P. Valavanis, A Survey of Controller Designs for New Generation UAVs: The Challenge of Uncertain Aerodynamic Parameters, International Journal of Control, Automation and Systems, pp. 1-16, 2019. https://doi.org/10.1007/s12555-018-0489-8
• T.E.Fraire, A. Dzul, F. C.Martínez, and W. Giernacki, Real-time Implementation and Flight Tests using Linear and Nonlinear Controllers for a Fixed-wing Miniature Aerial Vehicle (MAV). Int. J. Control Autom. Syst., 16, 392–396, 2018. https://doi.org/ 10.1007/s12555-016-0590-9
• Y. C. Wang, W. S. Chen, S. X. Zhang, J. W. Zhu and L. J. Cao, Command-filtered incremental backstepping controller for small unmanned aerial vehicles. Journal of Guidance, Control, and Dynamics, 41 (4), 954-967, 2018. https://doi.org/10.2514/1.G003001
• A. Brezoescu, T. Espinoza, P. Castillo and R. Lozano, Adaptive Trajectory Following for a Fixed-Wing UAV in Presence of Crosswind, J Intell Robot Syst, 69:257–271,2013. https://doi.org/10.1007/s10846-012-9756-8
• A.T. E. Fraire, Y. Chen, A. Dzul and R. Lozano, Fixed-wing MAV adaptive PD control based on a modified MIT rule with sliding-mode control, International Conference on Unmanned Aircraft Systems (ICUAS), pp. 1647-1656, Miami, FL, USA, 2017
• Z. Latif, A. Shahzad, A. I. Bhatti, J. F. Whidborne and R. Samar, Autonomous Landing of an UAV Using H∞ Based Model Predictive Control, Drones 6, 2022. https://doi.org/10.3390/drones6120416
• H. Zhuang , Q. Sun, Z. Chen and X. Zeng , Robust adaptive sliding mode attitude control for aircraft systems based on back-stepping method, Aerospace Science and Technology, 118, 1-18, 2021. https://doi .org/10.1016/j.ast.2021.107069
• U. Gunes, A. Sel, C. Kasnakoglu and U. Kaynak, Output Feedback Sliding Mode Control of a Fixed-Wing UAV Under Rudder Loss, AIAA Scitech 2019. https://doi.org/10.2514/6.2019-0911
• L. Cavanini,G. Ippoliti and E. F. Camacho, Model Predictive Control for a Linear Parameter Varying Model of an UAV, Journal of Intelligent & Robotic Systems,101:57,2022. https://doi.org/10.1007/s10846-021-01337-x
• S. R. Movahhed and M. A. Hamed, Output tracking of a 6-DOF flying wing UAV in longitudinal motion using LQR optimized sliding mode control with integral action,7th International Conference on Control, Instrumentation and Automation (ICCIA), pp. 1-5,2022. doi: 10.1109/ICCIA52082.2021.9403604
• M. Mammarella and E. Capello, A Robust MPC-based autopilot for mini UAVs, International Conference on Unmanned Aircraft Systems (ICUAS), pp. 1227–1235, 2018.https://doi.org/10.1109/ICUAS.2018.8453290
• H. Ülker, C. Baykara and C. Özsoy, Design of MPCs for a fixed wing UAV, Aircraft Engineering and Aerospace Technology, 89(6), pp.893901, 2017. https: //doi.org/10.1108/AEAT-08-2015-0198
• Nelson, R. C. (2007). Flight Stability and Automatic Control, 2nd ed., McGrawHill, New York
• Chen, P.; Zhang, G.; Li, J.; Chang, Z.; Yan, Q. Path-Following Control of Small Fixed-Wing UAVs under Wind Disturbance. Drones, 7,253,2017. https://doi.org/ 10.3390/drones7040253
• Li, F.; Song, W.P.; Song, B.F.; Jiao, J. Dynamic Simulation and Conceptual Layout Study on a Quad-Plane in VTOL Mode in Wind Disturbance Environment. Int. J. Aerosp. Eng., 2022, 5867825.
• Y. Çantaş and A. Akbulut, Sabit kanatlı hava araçları için otopilot tasarımı ve benzetimi, Politeknik Dergisi, 25(4), 1523-1534, 2021. doi:10.2339/politeknik.89479 6
• E. Çetin, System identification and control of a fixed wing aircraft by using flight data obtained from x-plane flight simulator, The Degree of Master of Science, The Graduate School of Natural and Applied Sciences of Middle East Technical University, Türkiye, 2018.
• J. Smith, J. Su, J. C. Liu and W. H. Chen, Disturbance observer based control with anti-windup applied to a small fixed wing UAV for disturbance rejection, Journal of Intelligent & Robotic Systems, 88, pp. 329-346, 2017. https://doi.org/10.1007/s10846-017-0534-5
• E. Ersoy and M. K. Yalçın , Designing autopilot system for fixed-wing flight mode of a tilt-rotor UAV in a virtual environment: X-Plane, International Advanced Researches and Engineering Journal, 2 (1),33-42, 2018.
• S. Mathisen, K. Gryte, S. Gros, et al. Precision Deep-Stall Landing of Fixed-Wing UAVs Using Nonlinear Model Predictive Control. J Intell Robot Syst 101 (24), 2021. https://doi.org/10.1007/s10846-020-01264-3
• U. Dursun, F. Y. Taşçıkaraoğlu and İ. Üstoğlu, An algebraic and suboptimal solution of constrained model predictive control via tangent hyperbolic function. Asian Journal of Control, 23 (5), 2420–2430, 2021. https://doi.org/10.1002/asjc.2357