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

Previous Articles    

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

RICH HTML

 10   

PDF (PC)

579

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
1 张国成, 姚彦龙, 王慧. 美国两级入轨水平起降可重复使用空天运载器发展综述[J].飞机设计, 2018, 38(2): 1-6.
Zhang Guo-cheng, Yao Yan-long, Wang Hui. A survey on development of two-stage-to-orbit horizontal take-off horizontal landing reusable launch vehicle in USA[J]. Aircraft Design, 2018, 38(2): 1-6.
2 谭永华, 李平, 杜飞平. 重复使用天地往返运输系统动力技术发展研究[J]. 载人航天, 2019, 25(1): 1-11.
Tan Yong-hua, Li Ping, Du Fei-ping. Research on development of propulsion technology for reusable space transportation system[J]. Manned Spaceflight, 2019, 25(1): 1-11.
3 Shahriar K, Maj D M. Six-DOF modeling and simulation of a generic hypersonic vehicle for conceptual design studies[C]∥AIAA Paper, 2004-4805.
4 Randall T, Lawrence D, Charles R. X-43A hypersonic vehicle technology development[J]. Acta Astronautic, 2006, 59(1-5): 181-191.
5 Keshmiri S, Colgren R, Mirmirani D. Six-DOF modeling and simulation of a generic hypersonic for control and navigation purposes[C]∥AIAA Paper, 2006-6694.
6 宗群, 田栢苓, 窦立谦, 等. 基于Gauss伪谱法的临近空间飞行器上升段轨迹优化[J]. 宇航学报,2010, 31(7) : 1775-1781.
Zong Qun, Tian Bai-ling, Dou Li-qian, et al. Ascent phase trajectory optimization for near space vehicle based on Gauss pseudospectral method[J]. Journal of Astronautics, 2010, 31(7): 1775-1781.
7 Pan B F, Lu P. Improvements to optimal launch ascent guidance[C]∥AIAA Paper, 2010-8174.
8 Fabrizio P, Edmondo M, Christie M,et al. Ascent trajectory optimization for a single-stage-to-orbit vehicle with hybrid propulsion[C]∥AIAA Paper: 2012-5828.
9 李惠峰, 李昭莹. 高超声速飞行器上升段最优制导间接法研究[J]. 宇航学报, 2011, 32(2): 297-309.
Li Hui-feng, Li Zhao-ying. Indirect method of optimal ascent guidance for hypersonic vehicle[J]. Journal of Astronautics, 2011, 32(2): 297-309.
10 万里鹏, 兰旭光, 张翰博, 等. 深度强化学习理论及其应用综述[J]. 模式识别与人工智能, 2019,32(1): 73-87.
Wan Li-peng, Lan Xu-guang, Zhang Han-bo, et al. A review of deep reinforcement learning theory and application[J]. Pattern Recognition and Artificial Intelligence, 2019, 32(1): 73-87.
11 Gaudet B, Furfaro R. Missile homing-phase guidance law design using reinforcement learning[J/OL]. [2022-09-18].
12 Yu X, Lv Z, Wu Y H, et al. Neural network modeling and backstepping control for quadrotor[J/OL]. [2022-09-18].
13 周宏宇, 王小刚, 赵亚丽, 等. 组合动力运载器上升段轨迹智能优化方法[J]. 宇航学报, 2020, 41(1):61-70.
Zhou Hong-yu, Wang Xiao-gang, Zhao Ya-li, et al. Ascent trajectory optimization for a multi-combined-cycle-based launch vehicle using a hybrid heuristic algorithm[J]. Journal of Astronautics, 2020, 41(1): 61-70.
14 刘扬, 何泽众, 王春宇. 基于DDPG算法的末制导律设计研究[J]. 计算机学报,2021, 44(9): 54-65.
Liu Yang, He Ze-zhong, Wang Chun-yu. Terminal guidance law design based on DDPG algorithm[J]. Chinese Journal of Computers, 2021, 44(9): 54-65.
15 Liang C, Wang W, Liu Z,et al. Learning to guide:guidance law based on deep meta-learning and model predictive path integral control[J]. IEEE Access, 2019, 7: 47353-47365.
16 张建强, 王振国, 李清廉. 空气深度预冷组合循环发动机吸气式模态建模及性能分析[J]. 国防科技大学学报, 2018, 40(1): 1-9.
Zhang Jian-qiang, Wang Zhen-guo, Li Qing-lian. Modeling and performance analysis of deeply precooled combined cycle engine in the air-breathing mode[J]. Journal of National University of Defense Technology, 2018, 40(1): 1-9.
17 Sutton R S, Barto A G. Reinforcement Learning:an Introduction[M].2nd Edition.Cambridge: MIT Press, 2018.
18 Wu Yu-hu, Shen Tie-long. Policy iteration algorithm for optimal control of stochastic logical dynamical systems[J]. IEEE Transactions on Neural Networks and Learning Systems, 2017(99): 1-6.
19 Gers F, Schmidhuber J, Cummins F. Learning to forget: continual prediction with LSTM[J]. Neural Computation, 2000, 12(10): 2451-2471.
20 Schulman J, Wolski F, Dhariwal P, et al. Proximal policy optimization algorithms[J/OL]. [2022-09-22].
21 Dossa R, Huang S Y, Ontanon S, et al. An empirical investigation of early stopping optimizations in proximal policy optimization[J/OL]. [2022-09-22].
[1] Yi YANG,Si⁃cai WANG,Ying NAN. Optimal algorithm of searching route for large amphibious aircraft [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(3): 963-971.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright © Journal of Jilin University(Engineering and Technology Edition), All Rights Reserved.
Tel: +86-431-85095297　Fax: +86-431-85094074　E-mail: xbgxb@jlu.edu.cn
Powered by Beijing Magtech Co. Ltd