To address the problem of real-time formation tracking control of multi-UAV systems, a highorder distributed real-time formation tracking control protocol is designed based on previous research. Firstly, the dynamic models of the third-order formation motion control subsystem and second-order formation attitude control subsystem are established based on the mathematical model with autopilot, and the algebraic graph theory is introduced. Secondly, considering the influence of the mass of the obstacle on the formation, the mass of the obstacle was introduced into the repulsive potential field function to improve the obstacle avoidance efficiency of the UAV. Then, according to the state information between the leader and follower of the UAV, as well as between the follower and its neighbors, a third-order real-time formation tracking control protocol is designed combined with the potential field and consensus theory. Finally, the stability is proved using the Lyapunov theory. The 3D simulation results show that the formation-tracking control accuracy and obstacle avoidance efficiency are improved by this control protocol.INDEX TERMS Consensus theory, formation tracking control, third-order integrator, the repulsive potential field function.

Multi-UAV cooperative trajectory planning model with a complex battlefield environment is difficult to establish, and the solution is complex. Aiming at these problems, a more realistic optimal combination model is established, and a particle swarm optimization with based on levy flight and differential evolution (LFPSO-DE) algorithm based on embedded Levy flight strategy is proposed. First, this work establishes a multi-UAV cooperative trajectory planning problem model in a complex environment, and establishes a comprehensive cost function to represent the complex environment; Secondly, aiming at the problem that the PSO algorithm is easy to fall into the local extremum, the Levy flight strategy is introduced to improve the particle swarm position update formula, and the inertia weight and learning factor are optimized, and then combined with the improved DE algorithm, the convergence speed and accuracy of the particle swarm are improved , while enabling the algorithm to have a better ability to explore; Finally, LFPSO-DE algorithm is compared with other algorithms in a variety of test functions, and then applied to multi-UAV trajectory planning experiments. The experimental results show that the LFPSO-DE algorithm has a good convergence speed and can effectively deal with the problem of falling into a local extremum. The planned trajectory is better in terms of voyage, speed, and time-consuming voyage, which has practical application value.