Document Type : Original/Review Paper

Authors

Department of Electrical Engineering, K.N. Toosi University of Technology, Tehran, Iran.

Abstract

This paper focuses on the design of advanced controllers and the implementation of magnetic tracking and velocity tracking at the position control and formation control levels for a group of quadcopters. Initially, PID controllers are developed based on the quadcopter structure, and then a constrained fuzzy-PID controller is introduced to steer the system to the desired position. The performance of this controller is compared with classical PID and fuzzy-PID controllers. This study examines the arrangement and formation coordination of six quadcopters under three different scenarios, evaluating their formation control and coordination. Each quadcopter has an internal controller responsible for maintaining formation accuracy and system stability. Due to the complexity of quadcopter dynamics, trajectory tracking is one of the most challenging research areas. In this regard, a fuzzy-PID controller is proposed to stabilize the quadcopter along predefined trajectories, utilizing speed information as input. Simulation results in the MATLAB/Simulink environment demonstrate that the fuzzy-PID controller outperforms the classical PID controller. Moreover, this controller exhibits greater resistance to external disturbances across all axes, higher accuracy in reducing tracking errors, and improved stability. This superiority is particularly evident in multi-agent systems, emphasizing the significance of advanced control techniques in enhancing the regulation of both single and multi-agent quadcopters. Ultimately, this improves tracking performance while ensuring dynamic efficiency in uncertain environments.

Keywords

Main Subjects

[1] A. R. Girard, A. S. Howell, and J. K. Hedrick, "Border patrol and surveillance missions using multiple unmanned air vehicles," in Proc. 43rd IEEE Conf. Decis. Control (CDC)*, vol. 1, 2004, pp. 620-625.
 
[2] Ö. Dündar, M. Bilici, and T. Ünler, "Design and performance analyses of a fixed wing battery VTOL UAV," Eng. Sci. Technol., Int. J., vol. 23, no. 5, pp. 1182-1193, 2020.
 
[3] M. M. Ferdaus et al., "Redpac: A simple evolving neuro-fuzzy-based intelligent control framework for quadcopter," in Proc. IEEE Int. Conf. Fuzzy Syst. (FUZZ-IEEE), 2019, pp. 1-7.
 
[4] S. Bouabdallah, A. Noth, and R. Siegwart, "PID vs LQ control techniques applied to an indoor micro quadrotor," in Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst. (IROS), vol. 3, 2004, pp. 2451-2456.
 
[5] P. E. I. Pounds, "Design, construction and control of a large quadrotor micro air vehicle," Ph.D. dissertation, Dept. Eng., Australian Nat. Univ., Canberra, Australia, 2007.
 
[6] P. Burggräf et al., "Quadrotors in factory applications: Design and implementation of the quadrotor's P-PID cascade control system," SN Appl. Sci., vol. 1, no. 7, p. 722, 2019.
 
[7] S. Abdelhay and A. Zakriti, "Modeling of a quadcopter trajectory tracking system using PID controller," Procedia Manuf., vol. 32, pp. 564-571, 2019.
 
[8] R. Miranda-Colorado and L. T. Aguilar, "Robust PID control of quadrotors with power reduction analysis," ISA Trans., vol. 98, pp. 47-62, 2020.
 
[9] D. Park et al., "Online tuning of PID controller using a multilayer fuzzy neural network design for quadcopter attitude tracking control," Front. Neurorobot., vol. 14, p. 619350, 2021.
 
[10] M. Davanipour et al., "Chaotic self-tuning PID controller based on fuzzy wavelet neural network model," Iran. J. Sci. Technol., Trans. Electr. Eng., vol. 42, pp. 357-366, 2018.
 
[11] T. Xu, "PID control and simulation of moving mass quadcopter UAV," in J. Phys.: Conf. Ser., vol. 2489, no. 1, 2023, p. 012015.
 
[12] H. Wang et al., "Power control in UAV-supported ultra dense networks: Communications, caching, and energy transfer," IEEE Commun. Mag., vol. 56, no. 6, pp. 28-34, 2018.
 
[13] H.-W. Lee and C.-S. Lee, "Research on logistics of intelligent unmanned aerial vehicle integration system," J. Ind. Inf. Integr., vol. 36, p. 100534, 2023.
 
[14] N. Abbas et al., "A survey: Future smart cities based on advance control of Unmanned Aerial Vehicles (UAVs)," Appl. Sci., vol. 13, no. 17, p. 9881, 2023.
 
[15] S. Sai et al., "A comprehensive survey on artificial intelligence for unmanned aerial vehicles," IEEE Open J. Veh. Technol., 2023.
 
[16] Y. Cao, W. Ren, and Z. Meng, "Decentralized finite-time sliding mode estimators and their applications in decentralized finite-time formation tracking," Syst. Control Lett., vol. 59, no. 9, pp. 522-529, 2010.
[17] X. Wang, V. Yadav, and S. Balakrishnan, "Cooperative UAV formation flying with obstacle/collision avoidance," IEEE Trans. Control Syst. Technol., vol. 15, no. 4, pp. 672-679, 2007.
 
[18] B. Yun et al., "Design and implementation of a leader-follower cooperative control system for unmanned helicopters," J. Control Theory Appl., vol. 8, pp. 61-68, 2010.
 
[19] R. Sharma and D. Ghose, "Collision avoidance between UAV clusters using swarm intelligence techniques," Int. J. Syst. Sci., vol. 40, no. 5, pp. 521-538, 2009.
 
[20] J. Wang and M. Xin, "Integrated optimal formation control of multiple unmanned aerial vehicles," IEEE Trans. Control Syst. Technol., vol. 21, no. 5, pp. 1731-1744, 2012.
 
[21] I. Bayezit and B. Fidan, "Distributed cohesive motion control of flight vehicle formations," IEEE Trans. Ind. Electron., vol. 60, no. 12, pp. 5763-5772, 2012.
 
[22] A. Kushleyev et al., "Towards a swarm of agile micro quadrotors," Auton. Robots, vol. 35, no. 4, pp. 287-300, 2013.
 
[23] R. W. Beard, J. Lawton, and F. Y. Hadaegh, "A coordination architecture for spacecraft formation control," IEEE Trans. Control Syst. Technol., vol. 9, no. 6, pp. 777-790, 2001.
 
[24] A. Abdessameud and A. Tayebi, "Formation control of VTOL unmanned aerial vehicles with communication delays," Automatica, vol. 47, no. 11, pp. 2383-2394, 2011.
 
[25] M. Turpin, N. Michael, and V. Kumar, "Decentralized formation control with variable shapes for aerial robots," in Proc. IEEE Int. Conf. Robot. Autom. (ICRA), 2012, pp. 23-30.
 
[26] A. Azarbahram et al., "Leader-Follower Formation Control of Uncertain USV Networks Under Stochastic Disturbances," Int. J. Ind. Electron. Control Optim., vol. 5, no. 2, pp. 133-142, 2022.
 
[27] S. Khankalantary, I. Izadi, and F. Sheikholeslam, "Robust ADP-based solution of a class of nonlinear multi-agent systems with input saturation and collision avoidance constraints," ISA Trans., vol. 107, pp. 52-62, 2020.
 
[28] N. H. Sahrir and M. A. Mohd Basri, "Modelling and manual tuning PID control of quadcopter," in Control, Instrumentation and Mechatronics: Theory and Practice, Springer, 2022, pp. 346-357.
 
 [29] F. Tatari and M. B. Naghibi-Sistani, "Optimal adaptive leader-follower consensus of linear multi-agent systems: Known and unknown dynamics," J. Artif. Intell. Data Min., vol. 11, no. 2, pp. 45-60, Jun. 2023. doi: 10.1234/jaidm.2023.123456.
 
[30] A. Surriani and M. Arrofiq, "Altitude control of quadrotor using fuzzy self tuning PID controller," in Proc. 5th Int. Conf. Instrum., Control, Autom. (ICA), 2017, pp. 67-72.
 
[31] T. J. Ross, Fuzzy Logic with Engineering Applications, 3rd ed. Hoboken, NJ: Wiley, 2005.
 
[32] E. Abbasi, M. Mahjoob, and R. Yazdanpanah, "Controlling of quadrotor UAV using a fuzzy system for tuning the PID gains in hovering mode," in Proc. 10th Int. Conf. Adv. Comput. Entertain. Technol., 2013, pp. 1-6.
 
[33] J.-W. Lee et al., "Adaptive altitude flight control of quadcopter under ground effect and time-varying load: Theory and experiments," J. Vib. Control, vol. 29, no. 3-4, pp. 571-581, 2023.
 
[34] M. De Queiroz, X. Cai, and M. Feemster, Formation Control of Multi-Agent Systems: A Graph Rigidity Approach. Hoboken, NJ: Wiley, 2019.