Parameterized evasion strategy for hypersonic glide vehicles against two missiles based on reinforcement learning

Abstract

In practical combat scenarios, Hypersonic Glide Vehicles (HGV) face the challenge of evading Successive Pursuers from the Same Direction while satisfying the Homing Constraint (SPSDHC). To address this problem, this paper proposes a parameterized evasion guidance algorithm based on reinforcement learning. The three-player optimal evasion strategy is firstly analyzed and approximated by parametrization. The switching acceleration command of HGV optimal evasion strategy considering the upper limit of missile acceleration command is analyzed based on the optimal control theory. The terminal miss of HGV in the case of evading two missiles is analyzed, which means that the three-player optimal evasion strategy is a linear combination of two one-to-one strategies. Then, a velocity control algorithm is proposed to increase the terminal miss by actively controlling the flight speed of the HGV based on the parametrized evasion strategy. The reinforcement learning method is used to implement the strategy in real time and a reward function is designed by deducing homing strategy for the HGV to approach the target, which ensures that the HGV satisfies the homing constraint. Experimental results demonstrate the feasibility and robustness of the proposed parameterized evasion strategy, which enables the HGV to generate maximum terminal miss and satisfy homing constraint when facing single or double missiles.