动态拓扑下四旋翼无人机集群蜂拥控制

In view of the cooperative control problem of unmanned aerial vehicle (UAV) swarm without strict configuration constraints under dynamic topology, a flocking control strategy of quadrotor UAV (QUAV) swarm is proposed on the basis of cascade control idea of the inner-outer loop of QUAV. The Kalman-consensus filter (KCF) algorithm is utilized to fuse the communication data with noise, which realizes the accurate estimation of the state of leader with varying velocity. Considering the dynamic variation and scalability requirements of UAV swarm, a flocking control algorithm based on the KCF is designed to realize the position control for the UAV swarm. The stability of the proposed algorithm is proved by the Lyapunov stability theory. An attitude controller is designed for QUAV based on brain emotional learning (BEL) model, which enables the pose control for QUAV. Simulation results prove the validity of control algorithm.