在线计算可重复使用火箭垂直着陆阶段的点火高度

Guanghui Cheng, W. Jing, C. Gao
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引用次数: 0

摘要

针对可重复使用火箭的垂直着陆阶段,为了在考虑多种不确定性的情况下提高着陆精度,提出了一种基于多项式制导法则(PGL)、粒子群优化(PSO)和深度强化学习(DRL)的在线计算点火高度的新策略。首先,设计了一个深度神经网络(DNN)来描述可重复使用火箭的状态与点火高度之间的关系。为了完成垂直着陆阶段的制导任务,通过引入估计的气动加速度对 PGL 进行了修改。通过仿真,修正后的 PSO 估算了 DNN 的输出范围。然后,通过 DRL 算法在模拟场景训练集上形成奖励函数并训练 DNN 的参数。最后,为了证明所提策略的有效性,利用训练好的 DNN 在线计算了 1500 个未学习模拟场景的点火高度。数值模拟结果表明,与基于改进 PSO 的离线固定点火高度策略相比,所提出的策略具有更高的着陆精度和更低的燃料消耗。
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Calculate the ignition height of the vertical landing phase online for the reusable rocket
For the vertical landing phase of the reusable rocket, in order to improve the landing accuracy with consideration of multiple uncertainties, a novel strategy to calculate the ignition height online is proposed based on polynomial guidance law (PGL), particle swarm optimization (PSO), and deep reinforcement learning (DRL). Firstly, a deep neural network (DNN) is designed to describe the relationship between the state of the reusable rocket and the ignition height. To accomplish the guidance task of the vertical landing phase, PGL is modified by introducing the estimated aerodynamic acceleration. Through simulation, the output range of the DNN is estimated by the modified PSO. Then, the reward function is shaped and the parameters of the DNN are trained on a training set of simulation scenarios by the DRL algorithm. Finally, to demonstrate the effectiveness of the proposed strategy, the trained DNN is used to calculate the ignition height of 1500 unlearned simulation scenarios online. The numerical simulation results show that the proposed strategy has higher landing accuracy and lower fuel consumption than the offline strategy of fixed ignition height based on the modified PSO.
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