Resumen
The mission of hypersonic vehicles faces the problem of highly nonlinear dynamics and complex environments, which presents challenges to the intelligent level and real-time performance of onboard guidance algorithms. In this paper, inverse reinforcement learning is used to address the hypersonic entry guidance problem. The state-control sample pairs and state-rewards sample pairs obtained by interacting with hypersonic entry dynamics are used to train the neural network by applying the distributed proximal policy optimization method. To overcome the sparse reward problem in the hypersonic entry problem, a novel reward function combined with a sophisticated discriminator network is designed to generate dense optimal rewards continuously, which is the main contribution of this paper. The optimized guidance methodology can achieve good terminal accuracy and high success rates with a small number of trajectories as datasets while satisfying heating rate, overload, and dynamic pressure constraints. The proposed guidance method is employed for two typical hypersonic entry vehicles (Common Aero Vehicle-Hypersonic and Reusable Launch Vehicle) to demonstrate the feasibility and potential. Numerical simulation results validate the real-time performance and optimality of the proposed method and indicate its suitability for onboard applications in the hypersonic entry flight.