In quadrotor formation tracking, the presence of unmodeled dynamics and external disturbances makes it highly challenging to simultaneously achieve accurate formation tracking and safe collision avoidance. To address these issues, a bearing-based formation control scheme for quadrotor swarms is proposed, integrating a parameter-adaptation method, a distributed extended state observer (DESO), and a reciprocal control barrier function (RCBF).First, within a leader–follower framework, a state–disturbance estimation architecture is developed by combining parameter adaptation with a distributed extended state observer. Based on the inter-UAV bearing angles, bearing-rate information, and relative distances, the proposed framework online estimates each follower’s global position and velocity, as well as the lumped internal and external disturbances.Subsequently, a control Lyapunov function (CLF) is constructed based on the estimated states and desired trajectories to ensure tracking of the desired positions and velocities. To address the inter-agent collision-avoidance requirement in quadrotor formations, a reciprocal control barrier function (RCBF) is designed that simultaneously considers relative positions and relative velocities, thereby achieving dynamic collision avoidance.On this basis, a formation-safe controller is formulated as a quadratic programming (QP) problem, where the optimal control input is computed in real time. The feasibility of the resulting QP is also analyzed. Simulation results demonstrate that, despite the influence of unmodeled dynamics and external disturbances, the proposed scheme can effectively accomplish formation establishment and maintenance tasks. The state–disturbance estimation framework provides accurate global
state and disturbance estimates. Comparative studies further verify the effectiveness of the collision-avoidance strategy and highlight
the overall advantages of the proposed control scheme.
ZHOU Wen-Yu
,
WANG Ying-Jie
,
WANG Ying-Jie Yang-ChenHao
,
ZHENG Zi-Xuan
,
YUE Xiao-Kui
. Bearing-based collision-avoidance formation control of quadrotor UAVs under internal and external disturbances[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 0
: 1
-0
.
DOI: 10.7527/S1000-6893.2026.33224
[1]杨明月, 寿莹鑫, 唐勇, 等.多四旋翼无人机编队保持与避碰控制[J].航空学报, 2022, 43(S1):89-99
[2]Tang Z, Cunha R, Hamel T, et al. Formation control of a leader–follower structure in three dimensional space using bearing measurements[J]. Automatica, 2021, 128: 109567.
[3]Zhao S, Zelazo D.Translational and Scaling Formation Maneuver Control via a Bearing-Based Approach[J].IEEE Transactions on Control of Network Systems, 2017, 4(3):429-438
[4]Li X, Wen C, Chen C.Adaptive Formation Control of Networked Robotic Systems With Bearing-Only Measurements[J].IEEE Trans Cybern, 2021, 51(1):199-209
[5]等.Bearing-Based Distributed Formation Control of Unmanned Aerial Vehicle Swarm by Quaternion-Based Attitude Synchronization in Three-Dimensional Space[J].Drones, 2022, 6(9):227-
[6]等.Bearing-Based Formation Tracking Control With Time-Varying Velocity Estimation[J].IEEE Trans Cybern, 2023, 53(6):3961-3973
[7]等.Bearing-Only Formation Control With Prespecified Convergence Time[J].IEEE Trans Cybern, 2022, 52(1):620-629
[8]等.Bearing-Only Formation Tracking Control of Multi-Agent Systems With Local Reference Frames and Constant-Velocity Leaders[J].IEEE Control Systems Letters, 2021, 5(1):1-6
[9]Erskine J, Balderas-Hill R, Fantoni I, 等.Model Predictive Control for Dynamic Quadrotor Bearing Formations. 见: 2021 IEEE International Conference on Robotics and Automation (ICRA). 2021. 124～130.
[10]Kabore K M, Guler S.Distributed Formation Control of Drones With Onboard Perception[J].IEEEASME Trans Mechatron, 2022, 27(5):3121-3131
[11]等.Control Schemes for Quadrotor UAV: Taxonomy and Survey[J].ACM Comput Surv, 2024, 56(5):1-32
[12]许海涛, 陈龙胜, 王宇翔, 等.四旋翼无人机编队飞行路径规划及分布式协同控制. 控制理论与应用, 2025: 1～10.
[13]Zhang H, Zhang X, Xu H, 等.Cooperative path following control of USV-UAVs with genetic algorithm extended state observer. Ocean Engineering, 2025, 320: 120332.
[14]Li C, Wang Y, Yang X.Adaptive fuzzy control of a quadrotor using disturbance observer. Aerospace Science and Technology, 2022, 128: 107784.
[15]Muliadi J, Kusumoputro B.Neural network control system of UAV altitude dynamics and its comparison with the PID control system[J].Journal of Advanced Transportation, 2018, 2018(1):3823201-
[16]Labbadi M, Cherkaoui M.Robust adaptive backstepping fast terminal sliding mode controller for uncertain quadrotor UAV. Aerospace Science and Technology, 2019, 93: 105306..
[17]Ames A D, Coogan S, Egerstedt M, 等.Control Barrier Functions: Theory and Applications. 见: 2019 18th European Control Conference (ECC). Naples, Italy: IEEE, 2019. 3420～3431.
[18]等.Control Barrier Function Based Quadratic Programs for Safety Critical Systems[J].IEEE Trans Automat Contr, 2017, 62(8):3861-3876
[19]Tan X, Cortez W S, Dimarogonas D V.High-Order Barrier Functions: Robustness,Safety,and Performance-Critical Control[J].IEEE Transactions on Automatic Control, 2022, 67(6):3021-3028
[20]付俊杰, 林潇坤, 温广辉.基于高阶控制障碍函数的多固定翼无人机鲁棒避障安全编队跟踪控制[J].机器人, 2025, 47(1):85-98
[21]Li J, Kang H, Li J, 等.Bearing-Based Collision-Free Formation Control for Spacecrafts Under Dynamic Event-Triggered Input. IEEE Transactions on Automation Science and Engineering, 2025, 22: 14855～14866.
[22]等.Uniform Finite Time Safe Path Tracking Control for Obstacle Avoidance of Autonomous Vehicle via Barrier Function Approach[J].IEEE Transactions on Intelligent Transportation Systems, 2024, 25(8):9512-9523
[23]Hassan K, Selvaratnam D, Sandberg H.On Resilience Guarantees by Finite-Time Robust Control Barrier Functions With Application to Power Inverter Networks. IEEE Open J Control Syst, 2024, 3: 497～513.
[24]Cheng H, Huang J.A General Framework for the Bearing-Based Formation Control[J].IEEE Transactions on Automatic Control, 2025, 70(6):3603-3616
CJA