基于强化学习的高超声速飞行器再入弹道设计

IF 4.6 Q1 OPTICS Journal of Physics-Photonics Pub Date : 2023-11-01 DOI:10.1088/1742-6596/2633/1/012005
Partha P Das, Wang Pei, Chenxi Niu
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引用次数: 0

摘要

在本研究中,我们研究了在连续空间中使用深度强化学习(DRL)对高超声速飞行器(HV)再入地球大气层后的控制。在此,我们将飞行器在大气飞行中的基本运动学方程和力方程结合起来,制定了满足边界约束和多个任务相关过程约束的再入轨道。飞行器的空气动力学模型模拟了普通飞行器(CAV-H)的特性,而地球的大气模型是基于1976年美国标准大气的标准模型,对行星模型进行了显著简化。在无动力飞行中,我们通过干扰飞行器的迎角和倾斜角来控制飞行器的轨迹,以达到期望的目标,其中控制问题基于不同的行为者批评框架,该框架利用神经网络(nn)作为函数逼近器来选择和评估连续状态和动作空间中的控制动作。首先,我们按照每一种方法训练模型,包括策略上的近端策略近似(PPO)和策略外的双延迟确定性策略梯度(TD3)。从生成的轨迹中,我们根据奖励模型为每个算法选择一个满足任务要求的标称轨迹。
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Reentry trajectory design of a hypersonic vehicle based on reinforcement learning
Abstract In this research, we investigate control of a hypersonic vehicle (HV) following its reentry into the Earth’s atmosphere, using deep reinforcement learning (DRL) in a continuous space. Here, we incorporate the basic kinematic and force equations of motion for a vehicle in an atmospheric flight to formulate the reentry trajectory satisfying the boundary constraints and multiple mission related process constraints. The aerodynamic model of the vehicle emulates the properties of a common aero vehicle (CAV-H), while the atmospheric model of the Earth represents a standard model based on US Standard Atmosphere 1976, with significant simplification to the planetary model. In an unpowered flight, we then control the vehicle’s trajectory by perturbing its angle of attack and bank angle to achieve the desired objective, where the control problem is based on different actor-critic frameworks that utilize neural networks (NNs) as function approximators to select and evaluate the control actions in continuous state and action spaces. First, we train the model following each of the methods, that include on-policy proximal policy approximation (PPO) and off-policy twin delayed deterministic policy gradient (TD3). From the trajectory generated, we select a nominal trajectory for each algorithm that satisfies our mission requirements based on the reward model.
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来源期刊
CiteScore
10.70
自引率
0.00%
发文量
27
审稿时长
12 weeks
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