Resumen
In this study, two different impact-angle-constrained guidance and control strategies using deep reinforcement learning (DRL) are proposed. The proposed strategies are based on the dual-loop and integrated guidance and control types. To address comprehensive flying object dynamics and the control mechanism, a Markov decision process is used to solve the guidance and control problem, and a real-time impact-angle error in the state vector is used to improve the model applicability. In addition, a reasonable reward mechanism is designed based on the state component which reduces both the miss distance and the impact-angle error and solves the problem of sparse rewards in DRL. Further, to overcome the negative effects of unbounded distributions on bounded action spaces, a Beta distribution is used instead of a Gaussian distribution in the proximal policy optimization algorithm for policy sampling. The state initialization is then realized using a sampling method adjusted to engineering backgrounds, and the control strategy is adapted to a wide range of operational scenarios with different impact angles. Simulation and Monte Carlo experiments in various scenarios show that, compared with other methods mentioned in the experiment in this paper, the proposed DRL strategy has smaller impact-angle errors and miss distance, which demonstrates the method?s effectiveness, applicability, and robustness.