Zhao, S.; Zhu, J.; Bao, W.; Li, X.; Sun, H. A Multi-Constraint Guidance and Maneuvering Penetration Strategy via Meta Deep Reinforcement Learning. Drones2023, 7, 626.
Zhao, S.; Zhu, J.; Bao, W.; Li, X.; Sun, H. A Multi-Constraint Guidance and Maneuvering Penetration Strategy via Meta Deep Reinforcement Learning. Drones 2023, 7, 626.
Zhao, S.; Zhu, J.; Bao, W.; Li, X.; Sun, H. A Multi-Constraint Guidance and Maneuvering Penetration Strategy via Meta Deep Reinforcement Learning. Drones2023, 7, 626.
Zhao, S.; Zhu, J.; Bao, W.; Li, X.; Sun, H. A Multi-Constraint Guidance and Maneuvering Penetration Strategy via Meta Deep Reinforcement Learning. Drones 2023, 7, 626.
Abstract
In response to the issue of vehicle escape guidance, this manuscript proposes a unified intelligent control strategy synthesizing optimal guidance and Meta Deep Reinforcement Learning (DRL). Optimal control with minor energy consumption is introduced to meet terminal latitude, longitude and altitude. Maneuvering escape is realized by adding longitudinal and lateral direction maneuver overloads. Maneuver command decision model is calculated based on Soft-Actor-Critic (SAC) networks. Meta learning is introduced to enhance autonomous escape capability, which improves generalization performance to time-varying scenarios not encountered in the training process. In order to obtain training samples at a faster speed, this manuscript uses the prediction method to solve reward values, which avoiding a large number of numerical integration. The simulation results manifest that the proposed intelligent strategy can achieve high precise guidance and effective escape.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
