The invention belongs to the technical field of aircraft navigation, guidance and control, and provides a satellite exploration control system and method based on deep reinforcement learning, and the system comprises a sensing layer, a decision layer, and an execution layer. The sensing layer is used for integrating equipment data and performing primary processing; the decision-making layer is used for deploying a deep reinforcement learning model and regularly carrying out data training and model updating; the execution layer is used for controlling satellite attitude and orbit adjustment and performing autonomous fault diagnosis and recovery operation; according to the method, effective control under different environments and emergencies is realized through continuous learning and optimization, the adaptive capacity and the autonomous control capacity are relatively high, and an efficient and accurate control strategy can be realized in a complex and changeable space environment through gradual optimization and iteration, so that the method is suitable for large-scale popularization and application. And the autonomous operation capability and the task execution efficiency of the satellite are greatly improved.
本发明属于飞行器导航、制导与控制技术领域,提供了一种基于深度强化学习的卫星探索控制系统及方法,包括感知层、决策层和执行层;感知层用于集成各设备数据,进行初步处理;决策层用于部署深度强化学习模型,并定期进行数据训练和模型更新;执行层用于控制卫星姿态和轨道调整,并进行自主故障诊断和恢复操作;本发明通过持续学习和优化,实现在不同环境和突发情况下的有效控制,具有较强的适应能力和自主控制能力,并通过逐步优化和迭代,能够在复杂多变的空间环境中实现高效、准确的控制策略,大幅提升卫星的自主运行能力和任务执行效率。
Satellite exploration control system and method based on deep reinforcement learning
一种基于深度强化学习的卫星探索控制系统及方法
2025-01-03
Patent
Electronic Resource
Chinese
IPC: | B64G Raumfahrt , COSMONAUTICS |
Satellite exploration control system and method based on deep reinforcement learning
European Patent Office | 2023
|Deep reinforcement learning based models for hard-exploration problems
European Patent Office | 2023
|Investigating exploration for deep reinforcement learning of concentric tube robot control
BASE | 2020
|