Synergistic Planning for Hypersonic Vehicles With an Analytic Hybrid Algorithm

This article researches synergistic planning for multiple hypersonic vehicles. Taking significant differences in vehicle parameters and synergistic demands into account, it is significantly challenging for computing a series of trajectories simultaneously in an efficient way. This challenge will become severer when there exists a high degree of nonlinear coupling between dynamics, thermal and load factors, constraints, and objective functions. To tackle with this problem, our proposal begins with a profile, which features natural satisfaction of terminal states, no-fly zones, and waypoints. Meanwhile, we research and conduct analytic dynamics using this profile for relieving the coupling. Then, we formulate a straightforward parameter-searching model to tackle the synergistic problem. In typical synergistic scenarios, this model can comprise more than 30 design variables, which further leads to challenges in initial guess, global searching, and convergence. Therefore, we propose a hybrid algorithm that combines particle swarm optimization (PSO) with reinforcement learning (RL). PSO is independent of initial guess and suitable for searching in high-dimensional spaces, but is typically lack of efficiency. While an RL strategy is featured with immediate network computation and PSO global searching after training of PSO-based offline synergistic trajectories. In the end, we carry out simulation in different synergistic scenarios for validating the effectiveness and robustness of our algorithm.