Adaptive dynamic programming-based spacecraft game control method and device under incomplete information

By combining adaptive dynamic programming and extended state observer with neural networks and superquadratic surface models, the time constraint and safety issues in spacecraft game control under incomplete information conditions are solved, and safe and efficient spacecraft game control in complex environments is achieved.

CN120756676APending Publication Date: 2025-10-10BEIHANG UNIV +1
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Patent Information

Application Number
CN202510951180.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-07-10
Publication Date
2025-10-10

AI Technical Summary

Technical Problem

Under conditions of incomplete information, there are problems of time constraints, safety and strategy unknowns in spacecraft game control, which are difficult to effectively solve with existing technologies. Especially in complex space environments, the dynamic behavior of spacecraft is affected by multiple factors and cannot effectively avoid collision risks.

Method used

An adaptive dynamic programming method is used to construct a fixed-time dilated state observer and the Ada-delta algorithm, combined with a neural network and a superquadratic surface model, to achieve real-time estimation of the unknown strategies and disturbances of the escaping spacecraft, and to design a safety-constrained game strategy to ensure near-optimal game control within a fixed time.

Benefits of technology

It improves the robustness and safety of spacecraft game control, ensures the achievement of mission objectives in complex environments, avoids collision risks, meets time constraints, and improves strategy convergence efficiency and stability.

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Abstract

The invention discloses a spacecraft game control method and device under incomplete information based on adaptive dynamic programming, and belongs to the technical field of spaceflight. The method comprises the following steps: establishing a spacecraft pursuit game relative motion model, and estimating an unknown strategy and spatial disturbance of a non-cooperative target through a fixed time expansion state observer; constructing an incomplete information pursuit game model based on the estimation state, designing an adaptive dynamic programming game strategy, and approaching an optimal game strategy by using a neural network; an Ada-delta method is introduced to adaptively adjust the learning rate of the neural network, so that the convergence efficiency is improved; an escape spacecraft shape model is constructed based on a super-quadric surface, and collision avoidance is ensured in combination with a potential function; a time constraint problem is solved through a fixed time compensation item, and an approximate optimal game strategy in fixed time is realized. According to the method, the problems of time constraint and strategy optimization of spacecraft game control under incomplete information are effectively solved while the safety is ensured.
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