基于学习的高超声速飞行器分层协调容错方法

doi:10.7527/S1000-6893.2024.30191

Abstract

Abstract:

To enhance fault-tolerance capability and mission completion ability of hypersonic vehicles under various severity levels of actuator faults， this paper proposes a learning-based hierarchical coordination fault-tolerant method for hypersonic vehicles. Firstly， to achieve online quantitative analysis of the severity of actuator faults， deep neural network-based approaches for predicting vehicle trim capability and reachable region boundaries are proposed. Subsequently， using the above prediction methods as “bridges”， a hierarchical coordinated fault-tolerance framework is constructed. The method assesses the severity of current actuator faults in real-time based on prediction results， then selectively coordinates fault-tolerant mechanisms at the control， guidance， and planning layers to mitigate the impact of actuator faults on flight performance and mission completion capability as much as possible. Finally， simulations under three different severity levels of actuator faults are conducted to validate the effectiveness of the proposed fault-tolerant method.

Key words: hypersonic vehicle, actuator fault, deep learning, trim capability, reachable region, fault tolerance

CLC Number:

V249.1

Tiancai WU, Honglun WANG, Bin REN, Guocheng YAN, Xingyu WU. Learning-based hierarchical coordination fault-tolerant method for hypersonic vehicles[J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(22): 330191.

Figures/Tables 24

Table 1

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Table 2

Actuator fault settings

故障情况	故障设置
1	$t = 100 s$ 时， $δ 1$ 出现 $λ 1 = 0$ 的有效性损失故障， $δ 2 ~ δ 8$ 不存在故障
2	$t = 100 s$ 时， $δ 1 ~ δ 4$ 出现 $λ i = 0$ 的有效性损失故障， $δ 5 ~ δ 8$ 不存在故障
3	$t = 100 s$ 时， $δ 1 ~ δ 4$ 出现 $ς i = 30 °$ 的有效性损失故障， $δ 5 ~ δ 8$ 不存在故障

Table 2

Table 3

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References 21

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对比方法	控制层	制导层	规划层
1	自抗扰方法容错控制方法^［21］	不考虑容错
2	本文所采用容错控制方法	不考虑容错
3	本文所采用容错控制方法	考虑可控能力的预测校正制导方法	不考虑容错

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Learning-based hierarchical coordination fault-tolerant method for hypersonic vehicles

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