Physicochemical-Neural Fusion for Semi-Closed-Circuit Respiratory Autonomy in Extreme Environments

Phillip Kingston; Nicholas Johnston

eess.SYMar 16, 2026

Physicochemical-Neural Fusion for Semi-Closed-Circuit Respiratory Autonomy in Extreme Environments

Authors: Phillip Kingston, Nicholas Johnston

Organizations: Member of Technical Staff, Galactic Bioware

Abstract

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.

Figures & tables

Figure 1: Control-oriented architecture of the semi-closed positive-pressure breathing loop. Solid lines illustrate gas flow and dashed lines sensor inputs / control links. Moist exhaled gas is drawn through the soda-lime scrubber first, then the downstream silica-gel dehumidifier, before O 2 replenishment and return to the breathing zone. Outward venting through one-way exhaust valves makes the suit semi-closed; the controller uses external, in-suit, and biometric sensing to regulate O 2 injection, fan speed, and scrubber bypass.

Sensor	Measurement	Range / Resolution
Radiant heat flux sensor	External thermal threat $\dot{q}_{\mathrm{rad}}$	0– $200\text{\,}\mathrm{kW}\text{\,}{\mathrm{m}}^{-2}$
External thermistor (shielded)	Ambient temperature $T_{\mathrm{ext}}$	$-$ 40 to $500\text{\,}\mathrm{\SIUnitSymbolCelsius}$ , $\pm$ $2\text{\,}\mathrm{\SIUnitSymbolCelsius}$
Toxic gas sensor (MOS array)	CO, HCN, NO x (external)	Multi-gas, semi-quantitative
Barometric pressure sensor	Ambient pressure $P_{a}$	800– $1100\text{\,}\mathrm{hPa}$ , $\pm$ $0.5\text{\,}\mathrm{hPa}$

Table 2

Sensor	Measurement	Range / Resolution
NDIR CO 2 sensor	In-suit $x_{\mathrm{CO_{2}}}$	0– $10\text{\,}\mathrm{\%}$ , $\pm$ $0.01\text{\,}\mathrm{\%}$
Galvanic O 2 ( $\times 3$ , median voting)	In-suit $x_{\mathrm{O_{2}}}$	0– $100\text{\,}\mathrm{\%}$ , $\pm$ $0.1\text{\,}\mathrm{\%}$
Capacitive RH sensor	In-suit relative humidity	0– $100\text{\,}\mathrm{\%}$ RH, $\pm$ $1.5\text{\,}\mathrm{\%}$
In-suit thermistor (breathing zone)	Breathing gas temperature $T_{\mathrm{suit,bz}}$	10 to $60\text{\,}\mathrm{\SIUnitSymbolCelsius}$ , $\pm$ $0.5\text{\,}\mathrm{\SIUnitSymbolCelsius}$
In-suit thermistor (torso)	Suit interior temperature $T_{\mathrm{suit,torso}}$	10 to $70\text{\,}\mathrm{\SIUnitSymbolCelsius}$ , $\pm$ $0.5\text{\,}\mathrm{\SIUnitSymbolCelsius}$
Thermocouple (K-type)	Scrubber bed temperature $T_{\mathrm{bed}}$	$-$ 40 to $1000\text{\,}\mathrm{\SIUnitSymbolCelsius}$ , $\pm$ $1\text{\,}\mathrm{\SIUnitSymbolCelsius}$

Table 3

Sensor	Measurement	Range / Resolution
Chest-strap ECG (dry electrode)	Heart rate (HR)	30– $240\text{\,}\mathrm{bpm}$ , $\pm$ $1\text{\,}\mathrm{bpm}$
Chest-strap ECG	Heart rate variability (HRV)	R–R intervals, $1\text{\,}\mathrm{ms}$
Torso IMU (accelerometer + gyro)	Activity level / posture	3-axis, $100\text{\,}\mathrm{Hz}$
Wrist PPG sensor	Redundant HR, perfusion index	30– $240\text{\,}\mathrm{bpm}$ , $\pm$ $3\text{\,}\mathrm{bpm}$
Wrist accelerometer	Wrist motion / activity	3-axis, $50\text{\,}\mathrm{Hz}$

Table 4

Figure 2: AI control architecture with explicit safety gating. External, in-suit, and biometric sensors feed the EKF-based sensor-fusion and state-estimation layer. The learned metabolic model provides physiological estimates to the MPC, while the RL policy advisor supplies a warm-start policy hint during nominal operation and a fallback candidate action if the MPC fails or times out. All candidate actuator commands pass through the control-barrier-function safety filter before reaching the actuators and, through them, the physical life-support plant and firefighter.

Panel of Figure 3 — Figure 3: Oxygen tank depletion curves for Scenario A. The MPC controller’s dynamic conservation extends endurance by modulating O 2 delivery rate as the tank depletes.

Safety-Critical Control

Safety-critical control designs controllers that keep physical systems within hard safety constraints despite uncertainty, disturbances, and limited actuation.

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Sep 18, 2026J/K move · Enter open · S save

Adaptive Uncertainty-Aware Modeling and Stochastic Radial Basis Function Predictive Control for Personalized Fluid Resuscitation

Elham Estiri, Hossein Mirinejad

School of Engineering, College of Aeronautics and Engineering, Kent State University, Kent, OH 44242 USA

Model Predictive Control Healthcare Adaptive Control State Space Models

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This paper presents a novel framework integrating Bayesian physiological modeling with optimal control strategies to achieve uncertainty-aware, personalized hemodynamic regulation during fluid resuscitation. An uncertainty-aware variational autoencoder state-space model (UVAE-SSM) was first developed to capture the dynamical relationship between mean arterial pressure (MAP) and fluid infusion using limited data, while explicitly modeling aleatoric uncertainty (i.e., randomness in the measurements, such as sensor noise). Then, a Bayesian nonlinear state-space model (BNSSM) was developed by utilizing Bayesian neural networks (BNNs) to capture epistemic uncertainty arising from physiological and patient-specific variability, enabling the creation of a virtual patient generator (VPG). Building on this uncertainty-aware modeling framework, a stochastic radial basis function model predictive control (sRBF-MPC) algorithm was designed to track the MAP target while satisfying physiological constraints. Finally, an online fine-tuning algorithm was developed to adapt the nominal UVAE-SSM using streaming VPG data, enabling progressive personalization during closed-loop therapy. Simulation results across unseen animal subjects and an independent human clinical dataset demonstrated the strong predictive accuracy and cross-population generalizability of the UVAE-SSM and BNSSM models. Closed-loop evaluations confirmed that the proposed sRBF-MPC framework achieved stable MAP regulation while providing better risk-aware control compared to quadratic MPC (Q-MPC) and stochastic quadratic MPC (sQ-MPC). Overall, the proposed framework accounts for inter- and intra-patient variability through online model adaptation, offering a promising step toward uncertainty-aware, personalized hemodynamic modeling and control in critical care.

Abstract

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