Satellite multi-pulse orbit transfer strategy optimization method

By modeling the satellite orbit change process as a Markov decision process and using deep reinforcement learning algorithms to optimize the satellite orbit change strategy, the problems of high computational complexity and difficulty in meeting high precision and timeliness in existing technologies are solved. The optimal balance between time and fuel consumption is achieved, making it suitable for complex satellite orbit optimization tasks.

CN122284281APending Publication Date: 2026-06-26CHINA ACADEMY OF SPACE TECHNOLOGY
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Applications(China)
Current Assignee / Owner
CHINA ACADEMY OF SPACE TECHNOLOGY
Filing Date
2025-12-09
Publication Date
2026-06-26

AI Technical Summary

Technical Problem

Existing satellite orbit change strategies suffer from high computational complexity and are prone to deviating from the actual optimal solution when dealing with complex orbit change tasks involving multiple pulses, multiple layers, and multiple constraints, making it difficult to meet the requirements of high-precision and high-timeliness satellite missions.

Method used

The satellite's pulse orbit change process is modeled as a Markov decision process. A deep reinforcement learning algorithm is used to jointly optimize the orbit change timing and pulse velocity increment. By constructing a state space, action space, and reward function, a satellite orbit change strategy is generated, and the optimal action is executed during on-orbit operation.

Benefits of technology

It achieves an optimal balance between time and fuel consumption, avoids local optima, has rapid decision-making capabilities, adapts to uncertainties and unexpected situations in orbital dynamics, and is suitable for solving high-dimensional and nonlinear problems.

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Abstract

This invention provides a method for optimizing a satellite multi-pulse orbit change strategy. It relates to the field of spacecraft orbit control, and the method steps are as follows: A Markov decision process model for satellite multi-pulse orbit change is constructed; the Markov decision process model is trained using a deep reinforcement learning algorithm to generate a satellite orbit change strategy; during on-orbit operation, based on the satellite's real-time state, the optimal action from the previous orbit change is output according to the satellite orbit change strategy, and the corresponding pulse orbit change operation is executed; where is the preset maximum number of pulse orbit changes; after completing the th pulse orbit change, the feasibility of a double-pulse orbit transfer from the current state to the destination orbit is determined, and the corresponding pulse orbit change task termination operation is executed based on the determination result.
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