基于改进粒子群算法的飞行器协同轨迹规划

This paper researches the synergistic flight for multiple hypersonic vehicles. The synergistic planning problem is formulated in view of the nonlinear coupling among aerodynamics, the performance index, and the path constraints. Then, the gliding profile, which naturally satisfies the terminal constraints and decreases the constraints, is proposed. Meanwhile, accurate solutions are deduced in the glide phase, so path constraints and the performance index can be quickly derived. An improved particle swarm optimization (PSO) method is developed by building the network between synergistic requirements and the optimal inertial weight in PSO based on a reinforcement learning method. Thus, the efficiency online computational efficiency can be largely improved. Numerical simulation results indicate the efficiency of the proposed method.