Trajectory Tracking of a Quadrotor Using Type-2 Neuro-Fuzzy Controllers

Yıl 2024, Cilt: 12 Sayı: 1, 40 - 56, 25.03.2024

Yeşim Öniz

https://doi.org/10.29109/gujsc.1311627

Öz

In this study, the trajectory tracking problem of a rotary wing unmanned aerial vehicle has been addressed by the use of type-2 neuro-fuzzy controllers. In order to determine the effectiveness of the developed control system, simulation and experimental studies have been performed for two different trajectories. The movement of the quadrotor in each direction has been controlled by a separate controller, and the difference between the actual and target positions for the relevant axis during the trajectory tracking along with the time derivative of this value has been fed to the controllers as the input signals. In order to better evaluate the results obtained, experimental and simulation studies for the same trajectories have been repeated with proportional-integral-derivative (PID) controllers and the responses of the controllers have been compared. Real-time experimental studies have been carried out indoors in a controlled environment with the Ar.Drone 2.0 produced by Parrot company. Especially the results recorded in the real-time experiments indicate that the proposed type-2 controllers with sliding mode control theory-based learning algorithm provide less steady-state error and more robust system response.

Anahtar Kelimeler

Quadrotor, type-2 neuro-fuzzy controller, sliding mode control

Proje Numarası

AK 85 089 0000

Tip-2 Nöro-Bulanık Denetleyiciler ile Döner Kanatlı İnsansız Hava Aracının Yörünge Takibi

Öz

Bu çalışmada, tip-2 nöro-bulanık denetleyiciler kullanılarak bir döner kanatlı insansız hava aracının yörünge takibi gerçekleştirilmiştir. Geliştirilen kontrol sisteminin etkinliğini belirlemek amacıyla, oluşturulan iki farklı yörünge için benzetim ve deneysel çalışmalar yapılmıştır. Her bir eksen için farklı bir denetleyici tasarlanmış olup hava aracının yörünge takibi sırasında ilgili eksen için gerçek ve hedef konumları arasındaki fark ve bu değerin zamana göre türevi denetleyicilerin giriş sinyalleri olarak kullanılmıştır. Elde edilen sonuçları daha iyi değerlendirebilmek amacıyla aynı yörüngeler için deneysel ve benzetim çalışmaları orantılı-integral-türev (PID) denetleyici ile tekrarlanmış olup denetleyicilerin cevapları karşılaştırılmıştır. Gerçek zamanlı deneysel çalışmalar, Parrot firması tarafından üretilen Ar.Drone 2.0 ile iç mekanda kontrollü bir ortamda gerçekleştirilmiştir. Özellikle deneysel çalışmalardan elde edilen sonuçlar, tip-2 nöro-bulanık denetleyiciler için geliştirilen kayma kipli kontrol tabanlı öğrenme algoritmalarının daha az kalıcı hal hatası ve daha gürbüz sistem cevabı sağladığını göstermektedir.

Anahtar Kelimeler

Döner kanatlı insansız hava aracı, tip-2 nöro-bulanık denetleyici, kayma kipli kontrol

Destekleyen Kurum

İstanbul Bilgi Üniversitesi

Proje Numarası

AK 85 089 0000

Teşekkür

Bu çalışma, İstanbul Bilgi Üniversitesi, Bilimsel Araştırma Projeleri birimi (BAP) tarafından maddi olarak desteklenmiştir.

Kaynakça

• [1] Mishra, Balmukund, et al. "Drone-surveillance for search and rescue in natural disaster." Computer Communications 156 (2020): 1-10.
• [2] Elmas, Elif Ece, and Mustafa ALKAN. "İnsansız Hava Araçlarıyla Hareketli Nesnelerin Tespit ve Takibi." Gazi University Journal of Science Part C: Design and Technology 10.4 (2022): 1111-1126.
• [3] Sarkar, Sayani, Michael W. Totaro, and Khalid Elgazzar. "Intelligent drone-based surveillance: application to parking lot monitoring and detection." In Unmanned Systems Technology XXI, vol. 11021, p. 1102104. International Society for Optics and Photonics, 2019.
• [4] Yazid, Edwar, Matthew Garratt, and Fendy Santoso. "Position control of a quadcopter drone using evolutionary algorithms-based self-tuning for first-order Takagi–Sugeno–Kang fuzzy logic autopilots." Applied Soft Computing 78 (2019): 373-392.
• [5] Mellinger D, Michael N, Kumar V. “Trajectory generation and control for precise aggressive maneuvers with quadrotors”. The International Journal of Robotics Research 2012; 31(5):664-74.
• [6] Bouabdallah S, Noth A, Siegwart R. “PID vs LQ control techniques applied to an indoor micro quadrotor”. In: 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS);Sendai, Japan; 2004. pp. 2451-2456.
• [7] Cowling ID, Yakimenko OA, Whidborne JF, Cooke AK. “Direct method based control system for an autonomous quadrotor”. Journal of Intelligent & Robotic Systems 2010; 60(2):285-316.
• [8] Alexis K, Papachristos C, Nikolakopoulos G, Tzes A. “Model predictive quadrotor indoor position control”. In 2011 19th Mediterranean Conference on Control & Automation (MED) 2011 Jun 20 (pp. 1247-1252). IEEE.
• [9] Abdolhosseini M, Zhang YM, Rabbath CA. “An efficcient model predictive control scheme for an unmanned quadrotor helicopter”. Journal of intelligent & robotic systems. 2013 Apr 1;70(1-4):27-38.
• [10] Stevens, Brian L., Frank L. Lewis, and Eric N. Johnson. Aircraft control and simulation: dynamics, controls design, and autonomous systems. John Wiley & Sons, 2015.
• [11] J. A. Meda, ‘‘Estimation of complex systems with parametric uncertainties using a JSSF heuristically adjusted.,’’ IEEE Latin Amer. Trans., vol. 16, no. 2, pp. 350–357, Feb. 2018.
• [12] J. A. Meda-Campana, ‘‘On the estimation and control of nonlinear systems with parametric uncertainties and noisy outputs,’’ IEEE Access, vol. 6, pp. 31968–31973, 2018.
• [13] Mehndiratta, Mohit, Erkan Kayacan, Mahmut Reyhanoglu, and Erdal Kayacan. "Robust tracking control of aerial robots via a simple learning strategy-based feedback linearization." Ieee Access 8 (2019): 1653-1669.
• [14] Yao, Wen, Xiaoqian Chen, Wencai Luo, Michel Van Tooren, and Jian Guo. "Review of uncertainty-based multidisciplinary design optimization methods for aerospace vehicles." Progress in Aerospace Sciences 47, no. 6 (2011): 450-479.
• [15] T. Dierks and S. Jagannathan, “Output feedback control of a quadrotor UAV using neural networks,” IEEE Trans. Neural Netw., vol. 21, no. 1,pp. 50–66, Jan. 2009.
• [16] M. Jafari and H. Xu, “Intelligent control for unmanned aerial systems with system uncertainties and disturbances using artificial neural network,”Drones, vol. 2, no. 3, 2018, Art. no. 30.
• [17] Al-Mahturi A, Santoso F, Garratt MA, Anavatti SG. “Nonlinear altitude control of a quadcopter drone using interval type-2 fuzzy logic”. In 2018 IEEE Symposium Series on Computational Intelligence (SSCI) 2018 Nov 18 (pp. 236-241). IEEE.
• [18] Prayitno A, Indrawati V, Utomo G. “Trajectory tracking of AR. Drone quadrotor using fuzzy logic controller”. Telekomnika. 2014;12(4):819-28.
• [19] Indrawati V, Prayitno A, Utomo G. “Comparison of two fuzzy logic controller schemes for position control of AR. Drone”. In 2015 7th International Conference on Information Technology and Electrical Engineering (ICITEE) 2015 Oct 29 (pp. 360-363). IEEE.
• [20] Dorzhigulov A., Bissengaliuly B., Spencer B. F. Jr, Kim J., James A. P. (2018). “ANFIS based quadrotor drone altitude control implementation on Raspberry Pi platform.” Analog Integrated Circuits and Signal Processing, 95(3), 435–445.
• [21] Ponce P., Molina A., Cayetano I., Gallardo J., Salcedo H., Rodriguez J., Carrera I. (2016). “Fuzzy logic sugeno controller type-2 for quadrotors based on anfis”. In Nature-Inspired Computing for Control Systems (2016): 195-230.
• [22] Krajnik T, Vonasek V, Fiser D, Faigl J. “AR-drone as a platform for robotic research and education”. In International conference on research and education in robotics 2011 Jun 15 (pp. 172-186). Springer, Berlin, Heidelberg.
• [23] Bristeau PJ, Callou F, Vissiere D, Petit N. “The navigation and control technology inside the ar. drone micro uav”. IFAC Proceedings Volumes. 2011 Jan 1;44(1):1477-84.
• [24] Jeurgens N. “Identification and control implementation of an AR. Drone 2.0”. Masters Thesis, Eindhoven University of Technology. 2017.
• [25] Y. Sun, “Modeling, identification and control of a quad-rotor drone using low-resolution sensing,” 2012.
• [26] Q. Li, “Grey-box system identification of a quadrotor unmanned aerial vehicle”. PhD thesis, Citeseer, 2014.
• [27] L. A. Zadeh, “The concept of a linguistic variable and its application to approximate reasoning—I,” Inf. Sci. (Ny)., vol. 8, no. 3, pp. 199–249, Jan. 1975.
• [28] J.M Mendel, “Uncertain Rule-based Fuzzy Logic System: Introduction and New Directions”, Prentice Hall, Upper Saddle River, 2001.
• [29] M. Biglarbegian, W. Melek, J. Mendel, “On the stability of interval type-2 TSK fuzzy logic control systems”, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 40 (3) (2010) 798–818.
• [30] Li, Long, Zuqiang Long, Hao Ying, and Zhijun Qiao. "An online gradient-based parameter identification algorithm for the neuro-fuzzy systems." Fuzzy Sets and Systems 426 (2022): 27-45.
• [31] Anshori, Mohamad Yusak, Dinita Rahmalia, Teguh Herlambang, and Denis Fidita Karya. "Optimizing Adaptive Neuro Fuzzy Inference System (ANFIS) parameters using Cuckoo Search (Case study of world crude oil price estimation)." In Journal of Physics: Conference Series, vol. 1836, no. 1, p. 012041. IOP Publishing, 2021.
• [32] Edwards, Christopher, and Sarah Spurgeon. “Sliding mode control: theory and applications”. Crc Press, 1998.
• [33] Lopez-Sanchez, Ivan, and Javier Moreno-Valenzuela. "PID control of quadrotor UAVs: A survey." Annual Reviews in Control 56 (2023): 100900.