Evasion guidance algorithms for air-breathing hypersonic vehicles in three-player pursuit-evasion games

Yan, Tianhong; Cai, Yuanli; Xu, Bin

doi:10.1016/j.cja.2020.03.026

Cited by 21 publications

(23 citation statements)

References 38 publications

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“…Remark 2 In the PE (pursuit-escape) game model, in order to simplify the derivation process, the dynamic equations of aircrafts can be expressed by first-order equations, and the dynamic parameters in the equations are constants. This way of expression can be seen in many studies [19,20,32,33,36,37] about PE (pursuit-escape) games.…”

Section: Remarkmentioning

confidence: 80%

“…It is assumed [19,20,32,33] that the dynamic equations of both sides can be linearized near the initial line of sight, and can be established:…”

Section: Pursuit-evasion Game Modelmentioning

confidence: 99%

“…Lateral maneuver can be carried out at a fixed altitude and fixed speed to avoid the impact on the attitude angle and angular velocity due to changes in speed and altitude during evasion. In some existing literature about the evasion of hypersonic vehicles, the modelling and simulations are conducted in 2-dimension [32,33].…”

Section: Remarkmentioning

confidence: 99%

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Evasion guidance for air-breathing hypersonic vehicles against unknown pursuer dynamics

Tian

Cai

2021

Neural Comput & Applic

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The rapid development of hypersonic vehicles has motivated the related research dramatically while the evasion of the hypersonic vehicles becomes one of the challenging issues. Different from the work based on the premise that the pursuers’ information is fully known, in this paper the evasion guidance for air-breathing hypersonic vehicles (AHVs) against unknown pursuer dynamics is studied. The gradient descent is employed for parameter estimation of the unknown dynamics of the pursuer. The energy-optimized evasion guidance algorithm is further developed by taking the acceleration constraint and energy optimization into consideration. Under the proposed algorithm, the system can deal with the unknown pursuer dynamics effectively and provide more practical guidance for the evasion process. The simulation results show that the proposed method can enable the AHV to achieve successful evasion.

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Section: Remarkmentioning

confidence: 80%

“…It is assumed [19,20,32,33] that the dynamic equations of both sides can be linearized near the initial line of sight, and can be established:…”

Section: Pursuit-evasion Game Modelmentioning

confidence: 99%

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Evasion guidance for air-breathing hypersonic vehicles against unknown pursuer dynamics

Tian

Cai

2021

Neural Comput & Applic

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“…According to References 44 and 45, the interval of

r\left({t}_f\right)

for achieving hit‐to‐kill accuracy is chosen as

\left[0,1.5\right]

m. in the interception cases of this paper.…”

Section: Simulationmentioning

confidence: 99%

Neural learning control of missile interception against dynamics uncertainty and target evasive maneuver

Yan

Guo

et al. 2022

Intl J Robust & Nonlinear

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The integrated guidance and control (IGC) design for the advanced interceptors against targets in progress of implementing evasive maneuvers is investigated. During interception, the target's unknown maneuver accelerations, and the extra angle of attack (AOA) caused by wind effects, are considered as disturbances. For the sake of enough hit‐to‐kill accuracy under multisource disturbances, the neural networks (NNs) and disturbance observer (DOB) are applied to solve the model inaccuracy and external disturbance. As a result, a novel DOB‐based neural controller can accomplish high tracking accuracy and hit‐to‐kill interception performance can be accomplished. Simulations studies demonstrate the validity and advantage bring by the DOB‐based neural control.

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“…Deception includes bait, stealth and interference, etc. Maneuvering penetration includes procedural, real-time intelligent maneuvers and multiple warhead maneuvers [3]. Compared with the traditional methods, intelligent strategy has advantages in online computing, wide applicability and strong robustness.…”

Section: Introductionmentioning

confidence: 99%

Research on Game-Playing Agents Based on Deep Reinforcement Learning

2022

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Path planning is a key technology for the autonomous mobility of intelligent robots. However, there are few studies on how to carry out path planning in real time under the confrontation environment. Therefore, based on the deep deterministic policy gradient (DDPG) algorithm, this paper designs the reward function and adopts the incremental training and reward compensation method to improve the training efficiency and obtain the penetration strategy. The Monte Carlo experiment results show that the algorithm can effectively avoid static obstacles, break through the interception, and finally reach the target area. Moreover, the algorithm is also validated in the Webots simulator.

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Evasion guidance algorithms for air-breathing hypersonic vehicles in three-player pursuit-evasion games

Cited by 21 publications

References 38 publications

Evasion guidance for air-breathing hypersonic vehicles against unknown pursuer dynamics

Evasion guidance for air-breathing hypersonic vehicles against unknown pursuer dynamics

Neural learning control of missile interception against dynamics uncertainty and target evasive maneuver

Research on Game-Playing Agents Based on Deep Reinforcement Learning

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