Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (3): 863-870.doi: 10.13229/j.cnki.jdxbgxb20221282

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Rapid trajectory programming for hypersonic umanned areial vehicle in ascent phase based on proximal policy optimization

Zhi-yong SHE(),Tong-ming ZHU,Wang-kui LIU   

  1. Beijing Institute of Aerospace Technology，The Third Academy of China CASIC，Beijing 100074，China
  • Received:2022-09-30 Online:2023-03-01 Published:2023-03-29

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Abstract:

Aiming at the problem that the online trajectory planning of high-speed unmanned aerial vehicle (UAV) in the ascending phase needs to realize online fast solution under multiple constraints, firstly, the motion and dynamics model of the vehicle was built, and the constraints faced by the trajectory planning were given. According to the constraints and flight characteristics, the action state space and reward evaluation function that meet the mission requirements were designed based on the near end strategy optimization (PPO) strategy gradient optimization. Secondly, based on the characteristics of strong time memory of the trajectory planning in the ascending phase of the aircraft, the short and long term memory network (LSTM) network structure was introduced on the basis of the traditional PPO algorithm, and the PPO-LSTM algorithm was used to solve the online trajectory planning problem in the ascending phase of the high-speed aircraft, and the model that can plan the optimal angle of attack strategy in real time according to the aircraft state was trained. Finally, the performance of the algorithm was verified by Monte Carlo simulation. The results show that the root-mean-square error of the terminal state of the algorithm in this paper is reduced by about 50% compared with the traditional PPO and particle swarm optimization, which fully proves the superiority and effectiveness of the proposed algorithm.

Key words: navigation guidance and control, hypersonic umanned areial vehicle, ascent phase, trajectory programming, proximal policy optimization algorithm

CLC Number: 

  • V19

Fig.1

Flight trajectory of hypersonic umanned areial vehicle"

Fig.2

LSTM networks"

Table 1

Parameter settings of LSTM"

参数数值参数数值
输入层节点数6网络堆叠层数2
隐藏层节点数1神经元随机丢弃数0
采用偏置True采用双向网络True

Table 2

Parameter setting of PPO"

参数数值参数数值
学习率0.003Gae lambda0.95
更新步长2048剪辑参数0.2
Batch size128损失的熵系数0
优化损失epoch10损失的价值函数系数0.5
折扣系数0.99梯度剪裁最大值0.5

Fig.3

Loss of PPO and PPO-LSTM"

Fig.4

Average rewards of PPO and PPO-LSTM"

Table 3

Parameter settings of model"

参数拉偏范围
初始轨迹倾角/（°）±3°
发动机推力/N±20%
升阻力/N±20%
大气密度/（kg·m-3）±20%
飞行器质量/kg±20%

Fig.5

Attack angle vs Ma"

Fig.6

Height vs time"

Fig.7

Height vs Ma"

Fig.8

Ma vs time"

Fig.9

Dynamic pressure vs time"

Table 4

Comparison among different methods"

算法

高度均方

误差/m

速度均方误差/（m·s-1）轨迹角均方误差/（°）
粒子群52.0286.7240.728
PPO31.6255.0910.111
PPO-LSTM25.7022.1740.078
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