考虑规避与突防的高超声速飞行器智能容错制导控制一体化设计

doi:10.7527/S1000-6893.2023.29607

Abstract

Abstract:

To address the problem of no-fly zone and interceptor avoidance during the reentry phase， a learning-based integrated fault-tolerant guidance and control method for hypersonic vehicles considering avoidance and penetration is proposed. Firstly， to improve the guidance performance of the vehicle in longitudinal guidance， the six degree of freedom model of the vehicle， which includes the attitude fault-tolerant control system， is adopted based on the integrated design concept of guidance and control to predict the range-to-go. In view of the resulting problem of time-consuming in calculation， the deep network is further used to fit the predicting process， so as to improve the real-timeliness of the algorithm. Then， to solve the problem of no-fly zone and interceptor avoidance in lateral guidance， the Interfered Fluid Dynamical System（IFDS） algorithm is introduced to calculate the expected heading angle of the vehicle considering threat factors， and the heading angle error corridor and the flip logic of the bank angle are used to obtain the command， so that the vehicle has the ability of avoidance and penetration. Next， considering the complexity of flight environment， the difference of maneuvering ability of vehicle during the reentry phase， and the influence of dynamic characteristics of attitude control system on guidance performance， the deep reinforcement learning algorithm is employed to train the agent， so that the agent can make online decisions on key parameters of IFDS algorithm according to the real-time state of vehicle， and improve the effectiveness and adaptability of the proposed guidance method. Finally， the results of semi-physical comparison simulation and Monte Carlo simulation show that the proposed method can achieve high performance in avoidance， penetration and guidance.

Key words: hypersonic vehicle, integrated guidance and control, avoidance, penetration, deep reinforcement learning

CLC Number:

V249.1

Tiancai WU, Honglun WANG, Bin REN, Yiheng LIU, Xingyu WU, Guocheng YAN. Learning-based integrated fault-tolerant guidance and control for hypersonic vehicles considering avoidance and penetration[J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(15): 329607-329607.

Figures/Tables 30

Fig.1

Fig.2

Fig.3

Fig.4

Table 1

Initial state dispersion deviation and parameter perturbation of vehicle

扰动项	分布类型	误差界
初始经度散布偏差 $Δ λ$ / $(°)$	均匀分布	±0.5
初始纬度散布偏差 $Δ ϕ$ / $(°)$	均匀分布	±0.5
初始高度散布偏差 $Δ h$ / $k m$	均匀分布	±2
初始速度散布偏差 $Δ V$ / $(m ⋅ s - 1)$	均匀分布	±100
初始弹道倾角散布偏差 $Δ θ$ / $(°)$	均匀分布	±0.2
初始弹道偏角散布偏差 $Δ σ$ / $(°)$	均匀分布	±0.5
气动系数 $η L 、 η x 、 η y 、 η z$ / $%$	均匀分布	±30
执行机构故障 $λ 1$ / $%$	均匀分布	0~100

Table 1

Fig.5

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Fig.7

Fig.8

Fig.9

Fig.10

Table 2

Central position， radius， and perturbation range of no-fly zones

编号	禁飞区中心位置/ $(°)$		半径/ $k m$	中心位置摄动范围/ $(°)$	分布类型
编号	经度	纬度	半径/ $k m$	中心位置摄动范围/ $(°)$	分布类型
1	10	8.5	180	0.3	均匀分布
2	14.5	6	180	0.3	均匀分布
3	20.5	11.5	180	0.3	均匀分布
4	22.5	8	180	0.3	均匀分布

Table 2

Fig.11

Fig.12

Fig.13

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Table 3

Guidance result statistics in comparative simulation

方法	终端位置误差/ $k m$	终端高度误差/ $k m$	终端速度误差/ $m ⋅ s - 1$	平均单步耗时/ $m s$
方法1	24.34	3.02	0.84	47.67
方法2	3.59	2.78	-17.54	11.36
方法3	4.01	0.86	5.04	14.41
所提方法	2.93	0.77	7.67	28.23

Table 3

Fig.23

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Table 4

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Learning-based integrated fault-tolerant guidance and control for hypersonic vehicles considering avoidance and penetration

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