Route Tracking Performance of Swarm Unmanned Aerial Vehicles (UAVs) with Fuzzy Logic Controller

Year 2020, Ejosat Special Issue 2020 (HORA), 272 - 278, 15.08.2020

Egemen Belge , Rıfat Hacıoğlu

https://doi.org/10.31590/ejosat.779958

Abstract

Swarm Unmanned Aerial Vehicles (UAVs) comprise of a group of aircraft that come together to achieve a specific goal. In recent years, the Swarm UAVs have been used in commercial, civil and military fields such as search and rescue operations, cargo transportation, sensitive agricultural practices, and ammunition delivery to war zones. Swarm UAVs can scan large areas in a short time in both military and civilian use. Swarm UAVs, which have the ability to communicate synchronously with each other, can perform complex tasks in a minimum energy and time by collaborating with respect to a single UAV. It is very important that swarm UAVs can follow the desired route with minimum error in order to perform the task in the shortest time and with least energy. In this study, the fuzzy logic controller is proposed for swarm quadrotors to follow the desired route with minimum error. The system modeling and mathematical equations of quadrotor have been developed in simulation environment. The performance of swarm UAVs to follow the rectangular and circular routes with minimum error is analyzed in this simulation. The fuzzy logic controller proposed for route tracking of the swarm UAVs is handled comparatively with the classical proportional-integral-derivative (PID) controller. The fuzzy logic controller developed in this simulation study increases the UAV’s sudden maneuverability and ability to complete the task with minimum energy compared to the classical PID controller. The classical PID and fuzzy controller performance of each UAV in the swarm is analyzed graphically and it is observed that the performance of the fuzzy logic controller to follow the reference route is higher than the classical PID controller.

Keywords

Swarm UAVs, Route Tracking, Quadrotor, PID Controller, Fuzzy Logic Controller

Bulanık Mantık Denetleyicisiyle Sürü İnsansız Hava Araçları (İHA)’nın Rota Takip Performansı

Abstract

Sürü İnsansız Hava Araçları (İHA) belirli bir hedefe ulaşmak için bir araya gelen bir grup hava aracından oluşmaktadır. Son yıllarda Sürü İHA'lar, arama kurtarma çalışmaları, yük taşımacılığı, hassas tarım uygulamaları, savaş bölgelerine mühimmat iletilmesi gibi ticari, sivil ve askeri alanlarda kullanılmaktadır. Sürü İHA'lar gerek askeri gerekse sivil kullanımlarda geniş alanları kısa sürede tarayabilmektedir. Birbiriyle eş zamanlı olarak iletişim kurma yeteneğine sahip sürü İHA’lar, işbirliği yaparak karmaşık görevleri tek bir İHA’ya göre minimum enerji ve sürede gerçekleştirebilir. Sürü İHA’ların istenen rotayı minimum hatayla takip edebilmesi, görevi en kısa sürede ve en az enerjiyle gerçekleştirebilmesi için oldukça önemlidir. Bu çalışmada, sürü quadrotorların istenen rotayı minimum hata ile takip edebilmeleri için bulanık mantık kontrolcüsü önerilmektedir. Quadrotorun sistem modellemesi ve matematiksel denklemleri benzetim ortamında geliştirilmektedir. Sürü İHA’ların minimum hata ile dikdörtgen ve dairesel rotaları takip etme performansı, bu benzetim ortamında analiz edilmektedir. Sürü İHA'ların rota takibi için önerilen bulanık mantık denetleyicisi, klasik PID denetleyicisiyle karşılaştırmalı olarak ele alınmaktadır. Bu benzetim çalışmasında geliştirilen bulanık mantık denetleyicisi, İHA’nın ani manevra kabiliyetini ve görevi minimum enerjiyle gerçekleştirebilme yeteneğini klasik PID denetleyicisine göre arttırmaktadır. Sürüdeki her bir İHA'nın klasik PID ve bulanık mantık denetleyici performansı yapılan çalışmada grafiksel olarak incelenmekte ve bulanık mantık denetleyicisinin referans rotayı takip edebilme performansı klasik PID denetleyicisine göre daha yüksek olduğu gözlemlenmektedir.

Keywords

Sürü İHAlar, Rota Takibi, Quadrotor, PID Denetleyicisi, Bulanık Mantık Denetleyicisi

References

• Abdelhay, S., & Zakriti, A. (2019). Modeling of a Quadcopter Trajectory Tracking System Using PID Controller. Procedia Manufacturing, 32, 564–571. https://doi.org/10.1016/j.promfg.2019.02.253
• Cabecinhas, D., Cunha, R., & Silvestre, C. (2014). A nonlinear quadrotor trajectory tracking controller with disturbance rejection. Control Engineering Practice, 26(1), 1–10. https://doi.org/10.1016/j.conengprac.2013.12.017
• Cheein, F. A., & Scaglia, G. (2014). Trajectory Tracking Controller Design for Unmanned Vehicles: A New Methodology. Journal of Field Robotics, 31(6), 861–887. https://doi.org/10.1002/rob.21492
• Chung, S. J., Paranjape, A. A., Dames, P., Shen, S., & Kumar, V. (2018). A Survey on Aerial Swarm Robotics. IEEE Transactions on Robotics, 34(4), 837–855. https://doi.org/10.1109/TRO.2018.2857475
• Gonzalez-Vazquez, S., & Moreno-Valenzuela, J. (2010). A New Nonlinear PI/PID Controller for Quadrotor Posture Regulation. 2010 IEEE Electronics, Robotics and Automotive Mechanics Conference, 642–647. https://doi.org/10.1109/CERMA.2010.78
• Hadaegh, F. Y., Chung, S. J., & Manohara, H. M. (2016). On development of 100-gram-class spacecraft for swarm applications. IEEE Systems Journal, 10(2), 673–684. https://doi.org/10.1109/JSYST.2014.2327972
• Idres, M., Mustapha, O., & Okasha, M. (2017). Quadrotor trajectory tracking using PID cascade control. IOP Conference Series: Materials Science and Engineering, 270(1). https://doi.org/10.1088/1757-899X/270/1/012010
• Joukhadar, A., AlChehabi, M., Stöger, C., & Müller, A. (2019). Trajectory Tracking Control of a Quadcopter UAV Using Nonlinear Control (pp. 271–285). https://doi.org/10.1007/978-3-319-89911-4_20
• Miller, D., Dasgupta, P., & Judkins, T. (2007). Distributed task selection in multi-agent based swarms using heuristic strategies. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4433 LNCS, 158–173. https://doi.org/10.1007/978-3-540-71541-2_11
• Selby, W. C. (2009). Autonomous Navigation and Tracking of Dynamic Surface Targets On-board a Computationally Impoverished Aerial Vehicle. In United States Naval Academy. https://s3-us-west-2.amazonaws.com/selbystorage/wp-content/uploads/2016/05/WCSelbyMSThesisFinal.pdf
• Siti, I., Mjahed, M., Ayad, H., & El Kari, A. (2019). New trajectory tracking approach for a quadcopter using genetic algorithm and reference model methods. Applied Sciences (Switzerland), 9(9). https://doi.org/10.3390/app9091780
• Sunay, A., Altan, A., Belge, E., & Hacıoğlu, R. (2020). Investigation of route tracking performance with adaptive PID controller in quadrotor. European Journal of Technic, 10(1), 160–172. https://doi.org/10.36222/ejt.652828
• Tahir, A., Böling, J., Haghbayan, M. H., Toivonen, H. T., & Plosila, J. (2019). Swarms of Unmanned Aerial Vehicles — A Survey. Journal of Industrial Information Integration, 16(August), 100106. https://doi.org/10.1016/j.jii.2019.100106
• Tan, Y., & Zheng, Z. yang. (2013). Research Advance in Swarm Robotics. Defence Technology, 9(1), 18–39. https://doi.org/10.1016/j.dt.2013.03.001
• Wu, X., & Liu, Y. (2018). Trajectory Tracking Control of Quadrotor UAV. 2018 37th Chinese Control Conference (CCC), 2018-July, 10020–10025. https://doi.org/10.23919/ChiCC.2018.8482939
• Zhao, Z. Y., Tomizuka, M., & Isaka, S. (1992). Fuzzy gain scheduling of PID controllers. Proceedings of the 1st IEEE Conference on Control Applications, CCA 1992, 23(5), 698–703. https://doi.org/10.1109/CCA.1992.269762