Numerical solution for elliptical orbit pursuit-evasion game via deep neural networks and pseudospectral method

Zhang, Cheng-ming; Zhu, Yanbo; Lee, Yang; Zeng, Xuanke; Zhang, Run-de

doi:10.1177/09544100221109980

Cited by 6 publications

(2 citation statements)

References 34 publications

(55 reference statements)

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“…Zhang et al [13] design a multi-objective globally optimal homing trajectory for a wing parachute based on the Gaussian pseudospectral method. Zhang et al [14] propose an improved Radau pseudospectral method combined with deep neural networks to solve the chase and escape game problem of orbital trajectory. Li et al [15] propose a hybrid optimization method based on the conjugate gradient method and pseudospectral point matching method for the optimal trajectory planning problem during rocket landing.…”

Section: Introductionmentioning

confidence: 99%

DDPG-Based Convex Programming Algorithm for the Midcourse Guidance Trajectory of Interceptor

Li,

et al. 2024

Aerospace

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To address the problem of low accuracy and efficiency in trajectory planning algorithms for interceptors facing multiple constraints during the midcourse guidance phase, an improved trajectory convex programming method based on the lateral distance domain is proposed. This algorithm can achieve fast trajectory planning, reduce the approximation error of the planned trajectory, and improve the accuracy of trajectory guidance. First, the concept of lateral distance domain is proposed, and the motion model of the midcourse guidance segment in the interceptor is converted from the time domain to the lateral distance domain. Second, the motion model and multiple constraints are convexly and discretely transformed, and the discrete trajectory convex model is established in the lateral distance domain. Third, the deep reinforcement learning algorithm is used to learn and train the initial solution of trajectory convex programming, and a high-quality initial solution trajectory is obtained. Finally, a dynamic adjustment method based on the distribution of approximate solution errors is designed to achieve efficient dynamic adjustment of grid points in iterative solving. The simulation experiments show that the improved trajectory convex programming algorithm proposed in this paper not only improves the accuracy and efficiency of the algorithm but also has good optimization performance.

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Section: Introductionmentioning

confidence: 99%

DDPG-Based Convex Programming Algorithm for the Midcourse Guidance Trajectory of Interceptor

Li,

et al. 2024

Aerospace

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“…Scholars turned to research the direct method which did not use the analytical necessary condition of both players 27 . In Reference 28, a deep neural networks ‐based Radau pseudospectral method was presented to solve the orbital pursuit‐evasion game. This method was more stable and faster than the traditional method.…”

Section: Introductionmentioning

confidence: 99%

Attack‐defense differential game to strength allocation strategies generation

Xiao

et al. 2023

Optim Control Appl Methods

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This paper addresses a difficult problem of strength allocation strategies generation with various adversaries and complex factors. Firstly, to investigate the strength allocation strategies generation problem, an attack‐defense differential game problem is formulated based on an improved Lanchester equation. Secondly, a numerical method, multi‐intervals simultaneous orthogonal collocation decomposition (MISOCD) method, is proposed to obtain the strength allocation strategies from the constructed model. Compared with the analytical method, MISOCD does not need to derive the necessary conditions. Thirdly, this study designs an approximated solution generation strategy based on adaptive learning pigeon‐inspired optimization algorithm to pregenerate the approximated strength allocation strategies in order to solve the initial value sensitivity problem. The approximated strategies are then used as the initial value guess of MISOCD method to generate optimal strength allocation strategies. Finally, two attack‐defense numerical simulations verify the effectiveness of strength allocation strategies generated by the proposed approach. Our proposed results provide a theoretical guide for both making attack‐defense strength allocation strategies and assessing confrontation actions.

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The Continuous Thrust Long-Orbit Satellite Pursuit-Evasion Game Control Using Feedback Genetic Shooting Method

Yang,

Tan,

Zhang

et al. 2024

Int. J. Aeronaut. Space Sci.

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Numerical solution for elliptical orbit pursuit-evasion game via deep neural networks and pseudospectral method

Cited by 6 publications

References 34 publications

DDPG-Based Convex Programming Algorithm for the Midcourse Guidance Trajectory of Interceptor

DDPG-Based Convex Programming Algorithm for the Midcourse Guidance Trajectory of Interceptor

Attack‐defense differential game to strength allocation strategies generation

The Continuous Thrust Long-Orbit Satellite Pursuit-Evasion Game Control Using Feedback Genetic Shooting Method

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