Stability Control of Multi-Quadcopter Formation Based on Virtual Leader and Flocking Algorithm

Authors

DOI:

https://doi.org/10.18196/jrc.v6i3.25598

Keywords:

Multi Quadcopter, Virtual Leader, Algoritma Flocking, Twisted Sliding Mode Control

Abstract

This study aims to develop an efficient and stable formation strategy for multi-quadcopter systems, focusing on formation stability based on the number of flying quadcopter members. The formation strategy combines a virtual leader approach and flocking-based behavior to achieve consistent formation movement. The formations are designed as basic circular and elliptical patterns based on bearing measurement. Formation control in multi-quadcopter systems presents a complex challenge, as it requires coordination among autonomously flying UAVs while maintaining overall formation stability and reliability. A Twisted Sliding Mode Control (TSMC) system is implemented to ensure formation stability and responsiveness to predefined trajectory missions. After integrating TSMC, the Root Mean Square Error (RMSE) of position errors in the x, y, and z coordinates decreased by 0.02.

References

P. Y. Leong and N. S. Ahmad, “Exploring Autonomous Load-Carrying Mobile Robots in Indoor Settings: A Comprehensive Review,” IEEE Access, vol. 12, pp. 131395–131417, 2024, doi: 10.1109/ACCESS.2024.3435689.

W. Malik and S. Hussain, “Developing of the smart quadcopter with improved flight dynamics and stability,” J. Electr. Syst. Inf. Technol., vol. 6, no. 1, pp. 1–8, 2019, doi: 10.1186/s43067-019-0005-0.

L. A. Fagundes, J. Kevin, B. D. C. Ricardo, and S. F. Alexandre, “Machine Learning for Unmanned Aerial Vehicles Navigation : An Overview,” SN Comput. Sci., 2024, doi: 10.1007/s42979-023-02592-5.

B. Mishra, D. Garg, P. Narang, and V. Mishra, “Drone-surveillance for search and rescue in natural disaster,” Comput. Commun., vol. 156, pp. 1–10, 2020, doi: 10.1016/j.comcom.2020.03.012.

A. Albanese, V. Sciancalepore, and X. Costa-Perez, “SARDO: An Automated Search-and-Rescue Drone-Based Solution for Victims Localization,” IEEE Trans. Mob. Comput., vol. 21, no. 9, pp. 3312–3325, 2022, doi: 10.1109/TMC.2021.3051273.

M. H. Rahman, M. A. S. Sejan, M. A. Aziz, R. Tabassum, J. I. Baik, and H. K. Song, “A Comprehensive Survey of Unmanned Aerial Vehicles Detection and Classification Using Machine Learning Approach: Challenges, Solutions, and Future Directions,” Remote Sens., vol. 16, no. 5, 2024, doi: 10.3390/rs16050879.

S. A. H. Mohsan, N. Q. H. Othman, Y. Li, M. H. Alsharif, and M. A. Khan, “Unmanned aerial vehicles (UAVs): practical aspects, applications, open challenges, security issues, and future trends,” Intell. Serv. Robot., vol. 16, no. 1, pp. 109–137, 2023, doi: 10.1007/s11370-022-00452-4.

J. Yousaf et al., “Drone and Controller Detection and Localization: Trends and Challenges,” Appl. Sci., vol. 12, no. 24, 2022, doi: 10.3390/app122412612.

K. Kim, “User Preferences in Drone Design and Operation,” Drones, vol. 6, no. 5, 2022, doi: 10.3390/drones6050133.

K. Telli et al., “A Comprehensive Review of Recent Research Trends on Unmanned Aerial Vehicles (UAVs),” Systems, vol. 11, no. 8, pp. 1–48, 2023, doi: 10.3390/systems11080400.

Z. Zhang, X. Xu, J. Cui, and W. Meng, “Multi-uav area coverage based on relative localization: Algorithms and optimal uav placement,” Sensors, vol. 21, no. 7, pp. 1–14, 2021, doi: 10.3390/s21072400.

S. M. S. Mohd Daud et al., “Applications of drone in disaster management: A scoping review,” Sci. Justice, vol. 62, no. 1, pp. 30–42, 2022, doi: 10.1016/j.scijus.2021.11.002.

N. Jia, Z. Yang, and K. Yang, “Operational effectiveness evaluation of the swarming UAVs combat system based on a system dynamics model,” IEEE Access, vol. 7, pp. 25209–25224, 2019, doi: 10.1109/ACCESS.2019.2898728.

J. Gui, T. Yu, B. Deng, X. Zhu, and W. Yao, “Decentralized Multi-UAV Cooperative Exploration Using Dynamic Centroid-Based Area Partition,” Drones, vol. 7, no. 6, pp. 1–15, 2023, doi: 10.3390/drones7060337.

X. An, C. Wu, Y. Lin, M. Lin, T. Yoshinaga, and Y. Ji, “Multi-Robot Systems and Cooperative Object Transport: Communications, Platforms, and Challenges,” IEEE Open J. Comput. Soc., vol. 4, 2022, pp. 23–36, 2023, doi: 10.1109/OJCS.2023.3238324.

V. G. Nair, J. M. D’souza, and K. R. Guruprasad, “Optimizing Multi-Agent Search with Non-Uniform Sensor Effectiveness in Distributed Quadcopter Systems.,” IEEE Access, vol. 12, pp. 85531–85550, 2024, doi: 10.1109/ACCESS.2024.3413596.

K. S. Bin Gaufan, S. El-Ferik, and N. M. Alyazidi, “Fractional Model-Based Formation Control of Quad-Rotor UAVs Using Sliding Mode Backstepping,” IEEE Access, vol. 12, 2024, doi: 10.1109/ACCESS.2024.3489630.

M. A. Al-Absi, A. A. Al-Absi, M. Sain, and H. Lee, “Moving ad hoc networks—a comparative study,” Sustain., vol. 13, no. 11, 2021, doi: 10.3390/su13116187.

M. A. Luna, M. Molina, R. Da-Silva-Gomez, J. Melero-Deza, P. Arias-Perez, and P. Campoy, “A multi-UAV system for coverage path planning applications with in-flight re-planning capabilities,” J. F. Robot., 2024, doi: 10.1002/rob.22342.

L. Chen and Z. Wang, “Multi-UAV trajectory planning for RIS-assisted SWIPT system under connectivity preservation,” Comput. Networks, vol. 255, p. 110906, 2024, doi: 10.1016/j.comnet.2024.110906.

J. P. Queralta et al., “Collaborative multi-robot search and rescue: Planning, coordination, perception, and active vision,” IEEE Access, vol. 8, pp. 191617–191643, 2020, doi: 10.1109/ACCESS.2020.3030190.

H. A. Hung, H. H. Hsu, and T. H. Cheng, “Optimal Sensing for Tracking Task by Heterogeneous Multi-UAV Systems,” IEEE Trans. Control Syst. Technol., vol. 32, no. 1, pp. 282–289, 2024, doi: 10.1109/TCST.2023.3298487.

J. Wang, K. Li, and K. Xia, “Distributed formation control of multi-UAV systems using relative information,” J. Franklin Inst., vol. 361, no. 10, p. 106945, 2024, doi: 10.1016/j.jfranklin.2024.106945.

X. Dong, Y. Hua, Y. Zhou, Z. Ren, and Y. Zhong, “Theory and Experiment on Formation-Containment Control of Multiple Multirotor Unmanned Aerial Vehicle Systems,” IEEE Trans. Autom. Sci. Eng., vol. 16, no. 1, pp. 229–240, 2019, doi: 10.1109/TASE.2018.2792327.

J. Alonso-Mora, E. Montijano, T. Nägeli, O. Hilliges, M. Schwager, and D. Rus, “Distributed multi-robot formation control in dynamic environments,” Auton. Robots, vol. 43, no. 5, pp. 1079–1100, 2019, doi: 10.1007/s10514-018-9783-9.

W. Pang, D. Zhu, and C. Sun, “Multi-AUV Formation Reconfiguration Obstacle Avoidance Algorithm Based on Affine Transformation and Improved Artificial Potential Field under Ocean Currents Disturbance,” IEEE Trans. Autom. Sci. Eng., vol. 21, no. 2, pp. 1469–1487, 2024, doi: 10.1109/TASE.2023.3245818.

L. Bian, W. Sun, and T. Sun, “Trajectory following and Improved Differential Evolution Solution for Rapid Forming of UAV Formation,” IEEE Access, vol. 7, pp. 169599–169613, 2019, doi: 10.1109/ACCESS.2019.2954408.

Q. Feng et al., “Resilience Measure and Formation Reconfiguration Optimization for Multi-UAV Systems,” IEEE Internet Things J., vol. 11, no. 6, pp. 10616–10626, 2024, doi: 10.1109/JIOT.2023.3326552.

S. Naderi and M. J. Blondin, “A Mapping and State-of-the-Art Survey on Multi-Objective Optimization Methods for Multi-Agent Systems,” IEEE Access, vol. 11, pp. 139728–139744, 2023, doi: 10.1109/ACCESS.2023.3341295.

M. Doostmohammadian, A. Taghieh, and H. Zarrabi, “Distributed Estimation Approach for Tracking a Mobile Target via Formation of UAVs,” IEEE Trans. Autom. Sci. Eng., vol. 19, no. 4, pp. 3765–3776, 2022, doi: 10.1109/TASE.2021.3135834.

B. Li et al., “Multi-UAV Trajectory Planning during Cooperative Tracking Based on a Fusion Algorithm Integrating MPC and Standoff,” Drones, vol. 7, no. 3, 2023, doi: 10.3390/drones7030196.

C. Tao, R. Zhang, Z. Song, B. Wang, and Y. Jin, “Multi-UAV Formation Control in Complex Conditions Based on Improved Consistency Algorithm,” Drones, vol. 7, no. 3, 2023, doi: 10.3390/drones7030185.

L. F. C. Ccari and P. R. Yanyachi, “A Novel Neural Network-Based Robust Adaptive Formation Control for Cooperative Transport of a Payload Using Two Underactuated Quadcopters,” IEEE Access, vol. 11, pp. 36015–36028, 2023, doi: 10.1109/ACCESS.2023.3265957.

J. Choi, Y. Song, S. Lim, C. Kwon, and H. Oh, “Decentralized multi-subgroup formation control with connectivity preservation and collision avoidance,” IEEE Access, vol. 8, pp. 71525–71534, 2020, doi: 10.1109/ACCESS.2020.2987348.

N. P. Nguyen et al., “Quadrotor Formation Control via Terminal Sliding Mode Approach: Theory and Experiment Results,” Drones, vol. 6, no. 7, 2022, doi: 10.3390/drones6070172.

M. B. Sial et al., “Bearing-Based Distributed Formation Control of Unmanned Aerial Vehicle Swarm by Quaternion-Based Attitude Synchronization in Three-Dimensional Space,” Drones, vol. 6, no. 9, 2022, doi: 10.3390/drones6090227.

L. Zhao, Y. Liu, Q. Peng, and L. Zhao, “A Dual Aircraft Maneuver Formation Controller for MAV/UAV Based on the Hybrid Intelligent Agent,” Drones, vol. 7, no. 5, 2023, doi: 10.3390/drones7050282.

Z. G. Xiong, Y. S. Luo, Z. Liu, and Z. K. Liu, “Suboptimal Relational Tree Configuration and Robust Control Based on the Leader-follower Model for Self-organizing Systems Without GPS Support,” Int. J. Control. Autom. Syst., vol. 22, no. 4, pp. 1442–1454, 2024, doi: 10.1007/s12555-022-0505-x.

L. Smolentsev, A. Krupa, and F. Chaumette, “Shape Visual Servoing of A Cable Suspended Between Two Drones,” IEEE Robot. Autom. Lett., vol. 9, no. 12, pp. 11473–11480, 2024, doi: 10.1109/LRA.2024.3494655.

X. Cai, X. Zhu, and W. Yao, “Distributed time-varying out formation-containment tracking of multi-UAV systems based on finite-time event-triggered control,” Sci. Rep., vol. 12, no. 1, pp. 1–16, 2022, doi: 10.1038/s41598-022-24083-y.

I. H. Imran, D. F. Kurtulus, A. M. Memon, S. Goli, T. Kouser, and L. M. Alhems, “Distributed Robust Formation Control of Heterogeneous Multi-UAVs With Disturbance Rejection,” IEEE Access, vol. 12, pp. 55326–55341, 2024, doi: 10.1109/ACCESS.2024.3390183.

R. Li, Y. Tang, and S. Li, “Robust Positive Consensus for Heterogeneous Multi-agent Systems,” Int. J. Control. Autom. Syst., vol. 22, no. 4, pp. 1129–1137, 2024, doi: 10.1007/s12555-022-1201-6.

X. Gao, C. Chen, and Z. Xiang, “Consensus for Nonlinear Multiagent Systems via Hybrid Event-Triggered Mechanism,” IEEE Syst. J., vol. 18, no. 4, pp. 2022–2029, 2024, doi: 10.1109/JSYST.2024.3488968.

T. Kwak, Y. Kim, Y. Hori, and T. H. Kim, “Graphical and Analytical Approaches for Analyzing Collective Behavior of Dynamic Multi-Agent Systems Governed by Generalized Cyclic Pursuit Mechanism,” IEEE Access, vol. 11, pp. 140481–140495, 2023, doi: 10.1109/ACCESS.2023.3339195.

Z. Pan, C. Zhang, Y. Xia, H. Xiong, and X. Shao, “An Improved Artificial Potential Field Method for Path Planning and Formation Control of the Multi-UAV Systems,” IEEE Trans. Circuits Syst. II Express Briefs, vol. 69, no. 3, pp. 1129–1133, 2022, doi: 10.1109/TCSII.2021.3112787.

Y. Tang, H. Chen, Z. Ma, Z. Jin, and H. Yin, “Application of Artificial Potential Field Method in Three-Dimensional Path Planning for UAV Considering 5G Communication,” IEEE Access, vol. 12, pp. 79238–79250, 2024, doi: 10.1109/ACCESS.2024.3406560.

L. Zhai, C. Liu, X. Zhang, and C. Wang, “Local Trajectory Planning for Obstacle Avoidance of Unmanned Tracked Vehicles Based on Artificial Potential Field Method,” IEEE Access, vol. 12, pp. 19665–19681, 2024, doi: 10.1109/ACCESS.2024.3355952.

J. Sun, J. Tang, and S. Lao, “Collision Avoidance for Cooperative UAVs with Optimized Artificial Potential Field Algorithm,” IEEE Access, vol. 5, pp. 18382–18390, 2017, doi: 10.1109/ACCESS.2017.2746752.

K. K. Oh, M. C. Park, and H. S. Ahn, “A survey of multi-agent formation control,” Automatica, vol. 53, pp. 424–440, 2015, doi: 10.1016/j.automatica.2014.10.022.

A. Singha, A. K. Ray, and A. B. Samaddar, “Leader–Follower Based Formation Controller Design for Quadrotor UAVs,” Trans. Indian Natl. Acad. Eng., vol. 7, no. 1, pp. 325–338, 2022, doi: 10.1007/s41403-021-00305-z.

C. Ren, F. Fu, C. Yin, Z. Yan, R. Zhang, and Z. Wang, “Improved artificial potential field method based on robot local path information,” Int. J. Adv. Robot. Syst., vol. 21, no. 5, pp. 1–16, 2024, doi: 10.1177/17298806241278172.

J. Yan, Y. Yu, Y. Xu, and X. Wang, “A Virtual Point-Oriented Control for Distance-Based Directed Formation and Its Application to Small Fixed-Wing UAVs,” Drones, vol. 6, no. 10, 2022, doi: 10.3390/drones6100298.

R. Olfati-Saber, “Flocking for multi-agent dynamic systems: Algorithms and theory,” IEEE Trans. Automat. Contr., vol. 51, no. 3, pp. 401–420, 2006, doi: 10.1109/TAC.2005.864190.

C. Song, L. Liu, and S. Xu, “Circle formation control of mobile agents with limited interaction range,” IEEE Trans. Automat. Contr., vol. 64, no. 5, pp. 2115–2121, 2019, doi: 10.1109/TAC.2018.2866985.

S. M. M. S. Sajadi and H. Atrianfar, “Dynamic Circular Formation Of Multi-Agent Systems With Obstacle Avoidance And Size Scaling: A Flocking Approach,” arXiv preprint arXiv:2212.12554, 2022.

S. Kim, H. Cho, and D. Jung, “Circular Formation Guidance of Fixed-Wing UAVs Using Mesh Network,” IEEE Access, vol. 10, pp. 115295–115306, 2022, doi: 10.1109/ACCESS.2022.3218673.

H. Su, S. Zhu, C. Chen, Z. Yang, and X. Guan, “Bearing-Based Robust Formation Tracking Control of Underactuated AUVs with Optimal Parameter Tuning,” IEEE Trans. Cybern., vol. 54, no. 7, pp. 4049–4062, 2024, doi: 10.1109/TCYB.2023.3346654.

Y. Li, P. Zhang, Z. Wang, D. Rong, M. Niu, and C. Liu, “Multi-UAV Obstacle Avoidance and Formation Control in Unknown Environments,” Drones, vol. 8, no. 12, 2024, doi: 10.3390/drones8120714.

P. Zhang, Z. Wang, Z. Zhu, Q. Liang, and J. Luo, Enhanced Multi-UAV Formation Control and Obstacle Avoidance Using IAAPF-SMC, vol. 8, no. 9, 2024, doi: 10.3390/drones8090514.

H. Li, J. Hu, Q. Zhou, and B. K. Ghosh, “Safe formation control of multiple unmanned aerial vehicles: control design and safety-stability analysis,” Control Theory Technol., vol. 22, no. 3, pp. 442–454, 2024, doi: 10.1007/s11768-024-00209-7.

Y. Liu, Z. Liu, G. Wang, C. Yan, X. Wang, and Z. Huang, “Flexible multi-UAV formation control via integrating deep reinforcement learning and affine transformations,” Aerosp. Sci. Technol., vol. 157, p. 109812, 2025, doi: 10.1016/j.ast.2024.109812.

C. Bao, Y. Guo, L. Luo, and G. Su, “Design of a Fixed-Wing UAV Controller Based on Adaptive Backstepping Sliding Mode Control Method,” IEEE Access, vol. 9, pp. 157825–157841, 2021, doi: 10.1109/ACCESS.2021.3130296.

N. P. Agustina and P. A. Darwito, “Autonomous Quadcopter Trajectory Tracking and Stabilization Using Control System Based on Sliding Mode Control and Kalman Filter,” 2023 Int. Semin. Intell. Technol. Its Appl., pp. 489–493, 2023, doi: 10.1109/ISITIA59021.2023.10221176.

C. Kuchwa-Dube and J. O. Pedro, “Chattering performance criteria for multi-objective optimisation gain tuning of sliding mode controllers,” Control Eng. Pract., vol. 127, p. 105284, 2022, doi: 10.1016/j.conengprac.2022.105284.

Y. B. Shtessel, J. A. Moreno, and L. M. Fridman, “Twisting sliding mode control with adaptation: Lyapunov design, methodology and application,” Automatica, vol. 75, pp. 229–235, 2017, doi: 10.1016/j.automatica.2016.09.004.

B. Li, X. Gao, H. Huang, and H. Yang, “Improved adaptive twisting sliding mode control for trajectory tracking of an AUV subject to uncertainties,” Ocean Eng., vol. 297, p. 116204, 2024, doi: 10.1016/j.oceaneng.2023.116204.

B. Yan, P. Dai, R. Liu, M. Xing, and S. Liu, “Adaptive super-twisting sliding mode control of variable sweep morphing aircraft,” Aerosp. Sci. Technol., vol. 92, pp. 198–210, 2019, doi: 10.1016/j.ast.2019.05.063.

H. Tiaiba, M. E. H. Daachi, and T. Madani, “Real-time adaptive super twisting algorithm based on PSO algorithm: Application for an exoskeleton robot,” Robotica, vol. 42, no. 6, pp. 1816–1841, 2024, doi: 10.1017/S0263574724000547.

A. Nandanwar et al., “Stochastic Event-Based Super-Twisting Formation Control for Multiagent System Under Network Uncertainties,” IEEE Trans. Control Netw. Syst., vol. 9, no. 2, pp. 966–978, 2022, doi: 10.1109/TCNS.2021.3089142.

H. Gao, W. Li, and H. Cai, “Fully Distributed Robust Formation Flying Control of Drones Swarm Based on Minimal Virtual Leader Information,” Drones, vol. 6, no. 10, 2022, doi: 10.3390/drones6100266.

B. Yildiz, M. F. Aslan, A. Durdu, and A. Kayabasi, “Consensus-based virtual leader tracking swarm algorithm with GDRRT*-PSO for path-planning of multiple-UAVs,” Swarm Evol. Comput., vol. 88, p. 101612, 2024, doi: 10.1016/j.swevo.2024.101612.

J. A. Vazquez Trejo et al., “Robust Formation Control Based on Leader-Following Consensus in Multi-Agent Systems with Faults in the Information Exchange: Application in a Fleet of Unmanned Aerial Vehicles,” IEEE Access, vol. 9, pp. 104940–104949, 2021, doi: 10.1109/ACCESS.2021.3098303.

A. Ebrahimi and M. Farrokhi, “Multi-agent flocking with obstacle avoidance and safety distance preservation: a fuzzy potential-based approach,” Intell. Serv. Robot., vol. 17, no. 2, pp. 181–195, 2024, doi: 10.1007/s11370-023-00500-7.

J. Wu, Y. Ji, X. Sun, and W. Liang, “Virtual-leader Split/Rejoin-based Flocking Control With Obstacle Avoidance for Multi-agents,” Int. J. Control. Autom. Syst., vol. 22, no. 5, pp. 1680–1690, 2024, doi: 10.1007/s12555-022-0950-6.

V. Utkin, A. Poznyak, Y. Orlov, and A. Polyakov, “Conventional and high order sliding mode control,” J. Franklin Inst., vol. 357, no. 15, pp. 10244–10261, 2020, doi: 10.1016/j.jfranklin.2020.06.018.

Z. Wang and Y. Luo, “Elliptical Multi-Orbit Circumnavigation Control of UAVS in Three-Dimensional Space Depending on Angle Information Only,” Drones, vol. 6, no. 10, 2022, doi: 10.3390/drones6100296.

Q. Wu, Y. Zeng, and R. Zhang, “Joint Trajectory and Communication Design for Multi-UAV Enabled Wireless Networks,” IEEE Trans. Wirel. Commun., vol. 17, no. 3, pp. 2109–2121, 2018, doi: 10.1109/TWC.2017.2789293.

F. Zitouni, S. Harous, and R. Maamri, "A Distributed Approach to the Multi-Robot Task Allocation Problem Using the Consensus-Based Bundle Algorithm and Ant Colony System," in IEEE Access, vol. 8, pp. 27479-27494, 2020, doi: 10.1109/ACCESS.2020.2971585.

H. Li, H. Li, X. Li, and X. Li, “Distributed consensus of heterogeneous linear time-varying systems on UAVs-USVs coordination,” IEEE Trans. Circuits Syst. II Express Briefs, vol. 67, no. 7, pp. 1264–1268, 2020, doi: 10.1109/TCSII.2019.2928870.

N. P. Agustina, “Control System on Multi-Quadcopter Based Sliding Mode Control ( SMC ) - Extended Kalman Filter ( EKF ) to Generate Optimal Trajectory Tracking Stability,” 2024 Int. Conf. Smart Comput. IoT Mach. Learn., pp. 19–24, 2024, doi: 10.1109/SIML61815.2024.10578288.

Z. Wang, Y. Zou, Y. Liu, and Z. Meng, "Distributed Control Algorithm for Leader–Follower Formation Tracking of Multiple Quadrotors: Theory and Experiment," in IEEE/ASME Transactions on Mechatronics, vol. 26, no. 2, pp. 1095-1105, April 2021, doi: 10.1109/TMECH.2020.3017816.

B. Measurements et al., “Adaptive Formation Tracking Control of Multiple Vertical Takeoff and Landing UAVs With,” IEEE Trans. Cybern., vol. 54, no. 6, pp. 3491–3501, 2024, doi: 10.1109/TCYB.2023.3290726.

M. Ye, B. D. O. Anderson, and C. Yu, “Bearing-Only Measurement Self-Localization , Velocity Consensus and Formation Control,” IEEE Trans. Aerosp. Electron. Syst., vol. 53, no. 2, pp. 575–586, 2017, doi: 10.1109/TAES.2017.2651538.

L. Chen and Z. Sun, “Gradient-based bearing-only formation control: An elevation angle approach,” Automatica, vol. 141, p. 110310, 2022, doi: 10.1016/j.automatica.2022.110310.

Downloads

Published

2025-05-10

Issue

Section

Articles