Adaptive Regulation of Micro Closed-Loop Space Station Ecosystems Using Proximal Policy Optimization

Long-term orbital missions require highly independent life-support systems, maintaining ecological stability under limited external support and subsystem attenuation. Conventional strategies are typically based on fixed threshold rules or deterministic mass-balance models and cannot adapt to disturbances or multi-objective trade-offs between resource safety and environmental load. This paper proposes a reinforcement learning framework for managing micro closed-loop ecological systems under reliability decay and environmental constraints. A reinforcement learning controller based on Proximal Policy Optimization (PPO) is trained to learn stable regulation policies with random disturbances. A dynamic state model is constructed to describe the coupled and time-sequential elements, ranging from water storage, oxygen concentration, energy reserve, subsystem reliability to ecological load. Degradation propagation is integrated into the transition dynamics through reliability-dependent recycling efficiency. Simulation experiments demonstrate that the proposed policy maintains all resource variables within the safety margins while reducing ecological imbalance compared with traditional strategies. Consequently, the proposed framework provides an expandable control architecture for independent regulation of space station life-support systems.