物理化学-神经融合用于极端环境半闭环呼吸自主性
Physicochemical-Neural Fusion for Semi-Closed-Circuit Respiratory Autonomy in Extreme Environments
AI总结：
本文提出了一种结合物理化学原理和人工智能的半闭环呼吸系统，通过融合传感器层级和强化学习策略，优化极端环境下的呼吸自主性，提升耐力性能。
摘要：
本文介绍了一种由银河生物工具有限公司开发的生命支持系统，这是一种半闭环呼吸装置，设计用于集成到正压消防服中，并由人工智能控制系统管理。呼吸循环包括氢氧化锂二氧化碳吸收器、硅胶除湿器和纯氧补充，具有有限消耗品。单向排气阀维持正压，同时形成一个半闭环系统，向外排气逐渐消耗气体储备。第一部分从基本原理发展物理化学基础，包括状态一致的热化学、化学计量容量限制、吸附等温线以及来自消防安全和毒性方面的氧气管理限制。第二部分引入了融合三个传感器层级的人工智能控制架构，包括外部环境传感、内部服装大气传感（三重冗余氧气传感器和中位投票）以及消防员生物指标。控制器结合了基于 horizon 的模型预测控制（MPC）与学习代谢模型和强化学习（RL）策略顾问，所有候选执行器命令都必须通过最终控制屏障函数安全过滤器后才能到达硬件。该架构旨在在未知任务持续时间和疲劳剖面下优化性能。本文介绍了一种仅使用结构消防中可用的传感器的18状态、3控制非线性状态空间公式，具有三重冗余氧气传感和中位投票。最后，我们介绍了一种具有动态资源稀缺乘数、RL策略顾问用于预热启动以及最终控制屏障函数安全过滤器的MPC框架，所有执行器命令必须通过该过滤器，模拟中比PID基线提升了18-34%的耐力性能，同时保持更紧密的生理和消防安全边际。
英文摘要：
This paper introduces Galactic Bioware's Life Support System, a semi-closed-circuit breathing apparatus designed for integration into a positive-pressure firefighting suit and governed by an AI control system. The breathing loop incorporates a soda lime CO2 scrubber, a silica gel dehumidifier, and pure O2 replenishment with finite consumables. One-way exhaust valves maintain positive pressure while creating a semi-closed system in which outward venting gradually depletes the gas inventory. Part I develops the physicochemical foundations from first principles, including state-consistent thermochemistry, stoichiometric capacity limits, adsorption isotherms, and oxygen-management constraints arising from both fire safety and toxicity. Part II introduces an AI control architecture that fuses three sensor tiers, external environmental sensing, internal suit atmosphere sensing (with triple-redundant O2 cells and median voting), and firefighter biometrics. The controller combines receding-horizon model-predictive control (MPC) with a learned metabolic model and a reinforcement learning (RL) policy advisor, with all candidate actuator commands passing through a final control-barrier-function safety filter before reaching the hardware. This architecture is intended to optimize performance under unknown mission duration and exertion profiles. In this paper we introduce an 18-state, 3-control nonlinear state-space formulation using only sensors viable in structural firefighting, with triple-redundant O2 sensing and median voting. Finally, we introduce an MPC framework with a dynamic resource scarcity multiplier, an RL policy advisor for warm-starting, and a final control-barrier-function safety filter through which all actuator commands must pass, demonstrating 18-34% endurance improvement in simulation over PID baselines while maintaining tighter physiological and fire-safety margins.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
