关键词: 四旋翼无人机, 方位编队, 分布式扩张状态观测器, 无人机防碰撞, 扰动补偿, 二次规划问题

Abstract: 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.

Key words: quadrotor, bearing-based formation, DESO, collision avoidance, disturbance compensation, quadratic programming problem