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UAV Formation Control under Fixed and Variable Adjacency Based Directed Network Topologies


Affiliations
1 Department of Electrical and Electronics Engineering, National Institute of Technology Sikkim, Ravangla, South Sikkim 737 139, India
2 National Institute of Technology Sikkim, Ravangla, South Sikkim 737 139, India
 

The UAV formation control is one of the key aspects in several applications like surveillance, moving target tracking, load-transportation, and delivery systems etc. These situations demand the multiple UAVs to manoeuvre in a desired formation. To address this problem, a distributed formation control scheme is proposed incorporating the details about the state of the neighbouring UAVs. The communication network topology among the UAVs is considered to be directed with the constant and the weighted adjacency matrices. The nonholonomic constraints are considered while deriving the desired Euler angles. Satisfying the conditions of Lyapunov provides necessary proof of stability along the positional and the attitude subsystems. Simulation results demonstrate that the desired tetrahedron, octahedron, and cube shapes are attained and maintained by the UAVs successfully. Also, the designed formation paradigm works proficiently for both the constant and the weighted adjacency matrices based directed network topologies. The performance validation is done through extensive comparative analysis for varying network connections.

Keywords

Graph Network, Laplacian Matrix, Lyapunov Stability, Nonholonomic Constraints, Weighted Adjacency Matrix.
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  • Julian K D & Kochenderfer M J, Distributed wildfire surveillance with autonomous aircraft using deep reinforcement learning, J Guid Control Dyn, 42(8) (2019) 1768–1778.

  • Izmaylov A Y, Smirov I, Kolesnikova V & Marchenko L, Substantiation of parameters of unmanned aerial vehicles for pesticides and fertilizers application in precision farming system, Mech agric Conserv resour, 63(5) (2017) 168–170.

  • Sun Z, Garcia D M H, Anderson B D & Yu C, Collaborative target-tracking control using multiple fixed-wing unmanned aerial vehicles with constant speeds, J Guid Control Dyn, 44(2) (2021) 238–250.

  • Hegde A & Ghose D, Multi-UAV distributed control for load transportation in precision agriculture, AIAA Scitech Forum 2020, 2068.

  • Rao S, Chakravarthy A & Ghose D, Planar manipulation of an object by unmanned aerial vehicles using sliding modes, J Guid Control Dyn, 44(1) (2021) 120–137.

  • Jang G, Kim J, Yu J K, Kim H J, Kim Y, Kim D W & Chung Y S, Cost-effective unmanned aerial vehicle (UAV) platform for field plant breeding application, Remote Sens, 12(6) (2020) 998.

  • Aljehani M & Inoue M, Performance evaluation of multiUAV system in post-disaster application: Validated by HITL simulator, IEEE Access, 7 (2019) 64386–64400.

  • De-Moraes R S & De-Freitas E P, Multi-UAV based crowd monitoring system, IEEE Trans Aerosp Electron Syst, 56(2) (2019) 1332–1345.

  • Yu Z, Zhang Y, Jiang B & Yu X, Fault-tolerant time-varying elliptical formation control of multiple fixed-wing UAVs for cooperative forest fire monitoring, J Intell Rob Syst, 101(3) (2021) 1–15.

  • Cokyasar T, Optimization of battery swapping infrastructure for e-commerce drone delivery, Comput Commun, 168 (2021) 146–154.

  • Zhu X, Zhang X X & Qu Y H, Consensus-based three-dimensional multi-UAV formation control strategy with high precision, Front Inf Technol Electron Eng, 18(7) (2017) 968–977.

  • Zhang J, Yan J & Zhang P, Multi-UAV formation control based on a novel back-stepping approach, IEEE Trans Veh Technol, 69(3) (2020) 2437–2448.

  • Zhang B, Sun X, Liu S, & Deng X, Formation control of multiple UAVs incorporating extended state observer-based model predictive approach, Int J Aeronaut Space Sci, 20(4) (2019) 953–963.

  • Zhang J, Wang W, Zhang Z, Luo K & Liu J. Cooperative control of UAV cluster formation based on distributed consensus, IEEE 15௧௛ Int Conf Cntrl Autom (ICCA) (IEEE) 2019, 788–793.

  • Zhang B, Sun X, Liu L & Deng X, Adaptive differential evolution-based distributed model predictive control for multi-UAV formation flight, Int J Aeronaut Space Sci, 21 (2019) 538–548.

  • Ju C & Son H L, A distributed swarm control for an agricultural multiple unmanned aerial vehicle system, Proc Inst Mech Eng I, 233(10) (2019) 1298–1308.

  • Singha A, Ray A K & Samaddar A B, Leader–follower based formation controller design for quadrotor UAVs, Trans Indian Natl Acad Eng. 7 (2022) 325–338.

  • Zhao Z, Wang J, Chen Y & Ju S, Iterative learning-based formation control for multiple quadrotor unmanned aerial vehicles, Int J Adv Rob Syst, 17(2) (2020) 1729881420911520.

  • Dutta R, Sun L & Pack D, A decentralized formation and network connectivity tracking controller for multiple unmanned systems, IEEE Trans Control Syst Technol, 26(6) (2017) 2206–2213.

  • Kartal Y, Subbarao K, Gans N R, Dogan A & Lewis F, Distributed backstepping based control of multiple UAV formation flight subject to time delays, IET Control Theory Appl, 14(12) (2020) 1628–1638.

  • Zhang W, Chaoyang D, Maopeng R & Yang L, Fully distributed time-varying formation tracking control for multiple quadrotor vehicles via finite-time convergent extended state observer, Chin J Aeronaut, 33(11) (2020) 2907–2920.

  • Xue R & Cai G, Formation flight control of multi-UAV system with communication constraints, J Aerosp Technol Management, 8(2) (2016) 203–210.

  • He L, Zhang J, Hou Y, Liang X & Bai P, Time-varying formation control for second-order discrete-time multi-agent systems with directed topology and communication delay, IEEE Access, 7 (2019) 33517–33527.

  • Pang S, Wang J, Liu J & Yi H, Three-dimensional leader–follower formation control of multiple autonomous underwater vehicles based on line-of-sight measurements using the back stepping method, Proc Inst Mech Eng I, 232(7) (2018) 819–829.

  • Mei F, Wang H, Yao Y, Fu J, Yuan X & Yu W, Robust second-order finite-time formation control of heterogeneous multi-agent systems on directed communication graphs, IET Control Theory Appl, 14(6) (2020) 816–823.

  • Rahimi R, Abdollahi F & Naqshi K, Time-varying formation control of a collaborative heterogeneous multi agent system, Rob Auton Syst, 62(12) (2014) 1799–1805.

  • Rabelo M F S, Brandao A S & Sarcinelli F M, Landing a UAV on static or moving platforms using a formation controller, IEEE Syst J, (2020) 1–9.

  • Zhao P, Wang W, Ying L, Sridhar B & Liu Y, Online multiple-aircraft collision avoidance method, J Guid Control Dyn, 43(8) (2020) 1456–1472.

  • Nair R R, Behera L, Kumar V & Jamshidi M, Multi satellite formation control for remote sensing applications using artificial potential field and adaptive fuzzy sliding mode control, IEEE Syst J, 9(2) (2014) 508–518.

  • Wang Z, Fei Q & Wang B, Distributed adaptive sliding mode formation control for multiple unmanned aerial vehicles, Chin Cntrl Decision Conf (CCDC) 2020, 2105–2110.

  • Liu Y, Qi N, Yao W, Zhao J & Xu S, Cooperative path planning for aerial recovery of a UAV swarm using genetic algorithm and homotopic approach, Appl Sci, 10(12) (2020) 4154.

  • Najm A A, Ibraheem I K, Azar A T & Humaidi A J, Genetic optimization-based consensus control of multi-agent 6-DoF UAV system, Sensors, 20(12) (2020) 3576.

  • Diwakar M, Singh P, Kumar P, Tiwari K & Bhushan S, A critical review on secure authentication in wireless network, Lecture Notes in Electrical Engineering (Springer) 768, (2022).

  • Chakraborty A, Jindal M, Khosravi M R, Singh P, Shankar A & Diwakar M, A secure IoT-based cloud platform selection using entropy distance approach and fuzzy set theory, Wirel Commun Mob Comput, 2021 (2021).

  • Saini D K J B, Patil P, Gupta K D, Kumar S, Singh P & Diwakar M, Optimized web searching using inverted indexing technique, In IEEE 11th Int Conf Commun Syst Netw Technol (CSNT) (IEEE) 2022, 351–356.

  • Wazid M, Bera B, Mitra A, Das A K & Ali R, Private blockchain-envisioned security framework for AI-enabled IoT-based drone-aided healthcare services, In Proc of the 2nd ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond (IEEE) 2020, 37–42.

  • Chen L, Li C, Sun Y & Ma G, Distributed finite-time tracking control for multiple uncertain Euler–Lagrange systems with input saturations and error constraints, IET Control Theory Appl, 13(1) (2018) 123–133.

  • Basri M A M, Design and application of an adaptive backstepping sliding mode controller for a six-DoF quadrotor aerial robot, Robotica, 36(11) (2018) 1701–1727.

  • Singha A, Ray A K & Samaddar A B, UGV-UAV coordination: Takeoff, landing and formation control, In IEEE World Autom Congr (WAC) (IEEE) 2021, 302–307.

  • Elhennawy A M & Habib M K, Trajectory tracking of a quadcopter flying vehicle using sliding mode control, 43௥ௗ Annual Conf of the IEEE Industrial Electronics Society (IEEE) 2017, 6264–6269.

  • Bouzid Y, Bestaoui Y, Siguerdidjane H & Zareb M, Quadrotor guidance-control for flight like nonholonomic vehicles, IEEE Int Conf Unmanned Aircraft Systems (ICUAS) 2018, 980–988.

  • Basri M A M, Husain A R & Danapalasingam K A, Enhanced backstepping controller design with application to autonomous quadrotor unmanned aerial vehicle, J Intell Rob Syst, 79(2) (2015) 295–321.

  • Li Y, Yang J & Zhang K, Distributed finite-time cooperative control for quadrotor formation, IEEE Access, 7 (2019) 66753–66763.


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  • UAV Formation Control under Fixed and Variable Adjacency Based Directed Network Topologies

Abstract Views: 48  |  PDF Views: 51

Authors

Arindam Singha
Department of Electrical and Electronics Engineering, National Institute of Technology Sikkim, Ravangla, South Sikkim 737 139, India
Anjan Kumar Ray
Department of Electrical and Electronics Engineering, National Institute of Technology Sikkim, Ravangla, South Sikkim 737 139, India
Arun Baran Samaddar
National Institute of Technology Sikkim, Ravangla, South Sikkim 737 139, India

Abstract


The UAV formation control is one of the key aspects in several applications like surveillance, moving target tracking, load-transportation, and delivery systems etc. These situations demand the multiple UAVs to manoeuvre in a desired formation. To address this problem, a distributed formation control scheme is proposed incorporating the details about the state of the neighbouring UAVs. The communication network topology among the UAVs is considered to be directed with the constant and the weighted adjacency matrices. The nonholonomic constraints are considered while deriving the desired Euler angles. Satisfying the conditions of Lyapunov provides necessary proof of stability along the positional and the attitude subsystems. Simulation results demonstrate that the desired tetrahedron, octahedron, and cube shapes are attained and maintained by the UAVs successfully. Also, the designed formation paradigm works proficiently for both the constant and the weighted adjacency matrices based directed network topologies. The performance validation is done through extensive comparative analysis for varying network connections.

Keywords


Graph Network, Laplacian Matrix, Lyapunov Stability, Nonholonomic Constraints, Weighted Adjacency Matrix.

References