Research on UAV Formation Control under Deception Attacks
DOI:
https://doi.org/10.54097/7ncyst56Keywords:
UAV Formation Control, Deception Attacks, Network Security, Multi-agent SystemsAbstract
With the rapid development of UAV technology and the continuous expansion of application scenarios, UAV formation control has become an important research direction in multi-agent systems. However, in complex network environments, deception attacks pose a serious threat to the security and stability of UAV formations. This paper systematically reviews the current research status of UAV formation control under deception attacks, providing an in-depth analysis from multiple dimensions, including research background, technical challenges, control methods, and security defenses. First, the development history and key technologies of UAV formation control are introduced. Then, the characteristics of deception attacks and their impact on formation control are analyzed in detail. Finally, the challenges faced by current research and future development directions are summarized.
Downloads
References
[1] Grand View Research. Drone Market Size, Share & Trends Analysis Report By Component, By Product, By Technology, By Payload Capacity, By Power Source, By End Use, By Region, And Segment Forecasts, 2025-2030[R]. San Francisco: Grand View Research, Inc., 2024.
[2] Oh K, Park M ,Ahn H .A survey of multi-agent formation control[J].Automatica,2015,53424-440.
[3] Chen W, Liu J, Guo H, et al. Toward robust and intelligent drone swarm: Challenges and future directions[J]. IEEE Network, 2020, 34(4): 278-283.
[4] Liao F, Teo R, Wang J L, et al. Distributed formation and reconfiguration control of VTOL UAVs[J]. IEEE Transactions on Control Systems Technology, 2016, 25(1): 270-277.
[5] Mehran M , Magnus E .Graph Theoretic Methods in Multiagent Networks[M].Princeton University Press:2010-07-01.
[6] Chen Q, Wang T, Jin Y, et al. A UAV formation control method based on sliding-mode control under communication constraints[J]. Drones, 2023, 7(4): 231.
[7] Zhiruo Z, Lei C A O, Xiliang C, et al. UAV intelligent attack strategy generation model based on multi-agent game reinforcement learning[J]. Systems Engineering & Electronics, 2023, 45(10).
[8] Sarhan A, Qin S. Intelligent Control for Formation Flight of UAVs Based on ANFIS[C]//2017 2nd International Conference on Machinery, Electronics and Control Simulation (MECS 2017). Atlantis Press, 2016: 447-453.
[9] Kamel B, Oussama A. Cooperative Navigation and Autonomous Formation Flight for a Swarm of Unmanned Aerial Vehicle[C]//2021 5th International Conference on Vision, Image and Signal Processing (ICVISP). IEEE, 2021: 212-217.
[10] Pan Z, Zhang C, Xia Y, et al. An improved artificial potential field method for path planning and formation control of the multi-UAV systems[J]. IEEE Transactions on Circuits and Systems II: Express Briefs, 2021, 69(3): 1129-1133.
[11] Zhang P, Wang Z, Zhu Z, et al. Enhanced Multi-UAV Formation Control and Obstacle Avoidance Using IAAPF-SMC[J]. Drones (2504-446X), 2024, 8(9).
[12] Tsao K Y, Girdler T, Vassilakis V G. A survey of cyber security threats and solutions for UAV communications and flying ad-hoc networks[J]. Ad Hoc Networks, 2022, 133: 102894.
[13] Check Point Research. 2023 Global Intelligent Systems Security Landscape Report[R]. Tel Aviv: Check Point Software Technologies Ltd., 2023.
[14] DING, DERUI, HAN, QING-LONG, WANG, ZIDONG, et al. A Survey on Model-Based Distributed Control and Filtering for Industrial Cyber-Physical Systems[J]. IEEE transactions on industrial informatics,2019,15(5):2483-2499. DOI:10.1109/ TII. 2019.2905295.
[15] Ao W, Song Y, Wen C. Distributed secure state estimation and control for CPSs under sensor attacks[J]. IEEE transactions on cybernetics, 2018, 50(1): 259-269.
[16] LIU F, XIAO B, CHEN S, et al. A Preferential Recovery Method of Interdependent Networks under Load[J]. Journal of Electronics & Information Technology, 2020, 42(7): 1694-1701.
[17] Feng, Zhi, and Guoqiang Hu. "Distributed secure average consensus for linear multi-agent systems under DoS attacks." 2017 American control conference (ACC). IEEE, 2017.
[18] Yin T, Gu Z, Xie X. Observer-based event-triggered sliding mode control for secure formation tracking of multi-UAV systems[J]. IEEE Transactions on Network Science and Engineering, 2022, 10(2): 887-898.
[19] Xiao J, Feroskhan M. Cyber attack detection and isolation for a quadrotor UAV with modified sliding innovation sequences [J]. IEEE Transactions on Vehicular Technology, 2022, 71(7): 7202-7214.
[20] Miao F, Zhu Q, Pajic M, et al. A hybrid stochastic game for secure control of cyber-physical systems[J]. Automatica, 2018, 93: 55-63.
[21] Zou Y, Xia K, He W. Adaptive fault-tolerant distributed formation control of clustered vertical takeoff and landing UAVs[J]. IEEE Transactions on Aerospace and Electronic Systems, 2021, 58(2): 1069-1082.
[22] The 14th Five-Year Plan for National Economic and Social Development of the People’s Republic of China and the Outline of the Long-Term Objectives for 2035[M]. Beijing: People’s Publishing House, 2021.
[23] Ministry of Industry and Information Technology. “14th Five-Year Plan” Intelligent Manufacturing Development Plan[Z]. 2022-12-22.
[24] Chinese Society of Aeronautics. China UAV Development Report (2023) [R]. Beijing: Aviation Industry Press, 2023.Li S, Chen Y, Liu P X. Distributed fault detection and dynamic event-triggered consensus for heterogeneous multiagent systems under deception attacks[J]. IEEE Transactions on Circuits and Systems I: Regular Papers, 2023, 70(8): 3294-3304.
[25] Zhu Q, Bushnell L, Başar T. Resilient distributed control of multi-agent cyber-physical systems[C]//Control of Cyber-Physical Systems: Workshop held at Johns Hopkins University, March 2013. Springer International Publishing, 2013: 301-316.
[26] Yuan H, Xia Y, Yang H. Resilient state estimation of cyber-physical system with multichannel transmission under DoS attack[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2020, 51(11): 6926-6937.
[27] Saulnier K, Saldana D, Prorok A, et al. Resilient flocking for mobile robot teams[J]. IEEE Robotics and Automation letters, 2017, 2(2): 1039-1046.
[28] Xu Y, Di L, Chen Y Q. Consensus based formation control of multiple small rotary-wing UAVs[C]//International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. 2011, 54808: 909-916.
[29] Kuriki Y, Namerikawa T. Consensus-based cooperative formation control with collision avoidance for a multi-UAV system[C]//2014 American control conference. IEee, 2014: 2077-2082.
[30] Yuan D, Wang Y. Data driven model-free adaptive control method for quadrotor formation trajectory tracking based on rise and ISMC algorithm[J]. Sensors, 2021, 21(4): 1289.
[31] Ranjan P K, Sinha A, Cao Y, et al. Relational maneuvering of leader-follower unmanned aerial vehicles for flexible formation[J]. IEEE Transactions on Cybernetics, 2024.
[32] Liu R, Qu B, Wei T, et al. Research on UAV formation obstacle avoidance based on consistency control[C]//2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS). IEEE, 2023: 155-160.
[33] Guo J, Qi J, Wang M, et al. Distributed cooperative obstacle avoidance and formation reconfiguration for multiple quadrotors: Theory and experiment[J]. Aerospace Science and Technology, 2023, 136: 108218.
[34] Song S, Zhao T, Zheng J. Research on fault-tolerant algorithm for emergency medical rescue UAV formation[C]//2020 IEEE International Conference on E-health Networking, Application & Services (HEALTHCOM). IEEE, 2021: 1-5.
[35] Zhen R, Jin Y, Wu X, et al. Fault‐Tolerant Time‐Varying Formation Tracking Control for Unmanned Aerial Vehicle Swarm Systems with Switching Topologies[J]. Mathematical Problems in Engineering, 2021, 2021(1): 5519243.
[36] Hua L, Zhang J, Li D, et al. Fault-tolerant active disturbance rejection control of plant protection of unmanned aerial vehicles based on a spatio-temporal RBF neural network[J]. Applied Sciences, 2021, 11(9): 4084.
[37] Xiang X, Liu C, Su H, et al. On decentralized adaptive full-order sliding mode control of multiple UAVs[J]. ISA transactions, 2017, 71: 196-205.
[38] Fei Y, Sun Y, Shi P. Robust hierarchical formation control of unmanned aerial vehicles via neural-based observers[J]. Drones, 2022, 6(2): 40.
[39] Liu Z, Huang D, Li S, et al. Adaptive robust control of the UAV-USV heterogeneous system with unknown fractional-order dynamics under multiple disturbances[C]//2023 42nd Chinese Control Conference (CCC). IEEE, 2023: 5872-5877.
[40] Yang S, Yu W, Liu Z, et al. A Robust Hybrid Iterative Learning Formation Strategy for Multi-Unmanned Aerial Vehicle Systems with Multi-Operating Modes[J]. Drones, 2024, 8(8): 406.
[41] Raffo G V, de Almeida M M. Nonlinear robust control of a quadrotor UAV for load transportation with swing improvement [C]//2016 American control conference (ACC). IEEE, 2016: 3156-3162.
[42] Hu J, Lanzon A. Cooperative control of innovative tri-rotor drones using robust feedback linearization[C]//2018 UKACC 12th International Conference on Control (CONTROL). IEEE, 2018: 347-352.
[43] Ning J, Huang Y, Liu Z, et al. Adaptive Distributed Heterogeneous Formation Control for UAV-USVs with Input Quantization[J]. Journal of Marine Science and Engineering, 2024, 12(6): 975.
[44] Peng Y, Yan H, Rao K, et al. Distributed model predictive control for unmanned aerial vehicles and vehicle platoon systems: a review[J]. Intelligence & Robotics, 2024, 4(3): 293-317.
[45] Wu Z, Yang F, Zhang B, et al. Distributed Model Predictive Control for Multi-UAV Formation Systems[C]//2024 14th Asian Control Conference (ASCC). IEEE, 2024: 891-896.
[46] Thien R T Y, Kim Y. Decentralized formation flight via PID and integral sliding mode control[J]. Aerospace Science and Technology, 2018, 81: 322-332.
[47] Dou L, Cai S, Zhang X, et al. Event-triggered-based adaptive dynamic programming for distributed formation control of multi-UAV[J]. Journal of the Franklin Institute, 2022, 359(8): 3671-3691.
[48] Yue J, Qin K, Shi M, et al. Event-trigger-based finite-time privacy-preserving formation control for multi-uav system[J]. Drones, 2023, 7(4): 235.
[49] Yue J, Qin K, Shi M, et al. Event-trigger-based finite-time privacy-preserving formation control for multi-uav system[J]. Drones, 2023, 7(4): 235.
[50] Guo Y. Fixed-time disturbance observer based robust tracking control of wheeled mobile robot with multiple disturbances[J]. Measurement and Control, 2023, 56(9-10): 1626-1636.
[51] Liu S, Zhang R, Zhao D, et al. Adaptive dual-channel event-triggered fuzzy control for autonomous underwater vehicles with multiple obstacles environment[J]. IEEE Transactions on Intelligent Transportation Systems, 2024.
[52] Thuyen N A, Thanh P N N, Anh H P H. Distributed event-triggered adaptive finite-time formation control for multiple under-actuated AUVs using command filtered backstepping technique with prescribed performance[J]. European Journal of Control, 2025, 82: 101183.
[53] Ding K, Li Y, Quevedo D E, et al. A multi-channel transmission schedule for remote state estimation under DoS attacks[J]. Automatica, 2017, 78: 194-201.
[54] Wang J, Gao J, Wu P. Attack-resilient event-triggered formation control of multi-agent systems under periodic DoS attacks using complex Laplacian[J]. ISA transactions, 2022, 128: 10-16.
[55] Pan K, Lyu Y, Pan Q. Adaptive formation for multiagent systems subject to denial-of-service attacks[J]. IEEE Transactions on Circuits and Systems I: Regular Papers, 2022, 69(8): 3391-3401.
[56] Liu Y, Dong X, Ren Z, et al. Secure formation control for resilient multi‐agent cyber‐physical systems under DoS attacks and faults[J]. International Journal of Robust and Nonlinear Control, 2023, 33(6): 3607-3626.
[57] Zheng Y, Zhang L, Huang B, et al. Distributed secure formation control for autonomous surface vessels by performance adjustable event‐triggered mechanism[J]. International Journal of Robust and Nonlinear Control, 2023, 33(14): 8490-8507.
[58] Wang J, Li F, Zhang X, et al. Adversarial obstacle generation against lidar-based 3d object detection[J]. IEEE Transactions on Multimedia, 2023, 26: 2686-2699.
[59] Zhang Q, Zhang S, Liu Y, et al. Adaptive terminal sliding mode control for USV-ROVs formation under deceptive attacks[J]. Frontiers in Marine Science, 2024, 11: 1320361.
[60] Li J, Wang L, Xi J, et al. Induced attack on formation control of multiagent systems with prescribed reference trajectories[J]. International Journal of Robust and Nonlinear Control, 2024, 34(12): 8374-8397.
[61] Alzahrani A. Novel approach for intrusion detection attacks on small drones using ConvLSTM model[J]. IEEE Access, 2024.
[62] Wu G, Gao X, Wan K. Mobility control of unmanned aerial vehicle as communication relay to optimize ground-to-air uplinks[J]. Sensors, 2020, 20(8): 2332.
[63] Zhang J, Chuai G, Gao W. Power control and clustering-based interference management for UAV-assisted networks[J]. Sensors, 2020, 20(14): 3864.
[64] Huo M, Duan H, Fan Y. Pigeon-inspired circular formation control for multi-UAV system with limited target information [J]. Guidance, Navigation and Control, 2021, 1(01): 2150004.
[65] Yang Y, Wang W, Xu R, et al. AoI optimization for UAV-aided MEC networks under channel access attacks: A game theoretic viewpoint[C]//ICC 2022-IEEE International Conference on Communications. IEEE, 2022: 1-6.
[66] Suo W, Wang M, Zhang D, et al. Formation control technology of fixed-wing UAV swarm based on distributed ad hoc network[J]. Applied Sciences, 2022, 12(2): 535.
[67] Fei B, Bao W, Zhu X, et al. Autonomous cooperative search model for multi-UAV with limited communication network[J]. IEEE Internet of Things Journal, 2022, 9(19): 19346-19361.
[68] Han Z, Yang Y, Bilal M, et al. Smart optimization solution for channel access attack defense under UAV-aided heterogeneous network[J]. IEEE Internet of Things Journal, 2023, 10(21): 18890-18897.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Frontiers in Computing and Intelligent Systems
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
