In the modern warfare with increasingly complex operational tasks and changing battlefield environment, it has become a trend for multi unmanned aerial vehicles(UAVs) to cooperate to complete complex operational tasks. The key technology of multi-UAVs system involves multi-UAVs formation flight control and reconfiguration. We researched the problem of multi-UAVs formation control, reconfiguration, as well as path planning using particle swarm optimization (PSO) and ant colony optimization (ACO) algorithm. For the formation reconfiguration, we considered the reconfiguration in a dynamical environment and gave a method based on a diversity PSO algorithm. We also developed a multi-UAVs coordinated formation flight control platform. The simulation results showed that the intelligent algorithms such as PSO and ACO can solve formation control, formation reconfiguration and flight path planning problem well, and the diversity PSO algorithm has stronger global optimization ability.

Aiming at the cruise segment trajectory prediction problem of near space reentry gliding vehicle, a trajectory prediction algorithm based on control law prediction was proposed. By analyzing and deriving the unknowns in the trajectory prediction and studying the flight modes and control laws of the cruising segment, the feasibility of trajectory prediction was clarified. The discriminating methods of two kinds of flight trajectories are studied: balanced gliding and jumping gliding. On this basis, a trajectory prediction algorithm based on control law prediction was designed. The algorithm was verified by simulation. The influence of tracking time and tracking starting point on trajectory prediction accuracy was studied. The results showed that the algorithm had high accuracy for different types of trajectories; the tracking time had a great influence on trajectory prediction accuracy; but the impact of tracking start point was small.