The carbon dioxide removal system is the most critical system for controlling CO₂ mass concentration in long-term manned spacecraft. In order to ensure the controlling CO₂ mass concentration in the cabin within the allowable range, the state of CO₂ removal system needs to be estimated in real time. In this paper, the mathematical model is firstly established that describes the actual system conditions and then the Galerkin-based extended Kalman filter algorithm is proposed for the estimation of the state of CO₂. This method transforms partial differential equation to ordinary differential equation by using Galerkin approaching method, and then carries out the state estimation by using extended Kalman filter. Simulation experiments were performed with the qualification of the actual manned space mission. The simulation results show that the proposed method can effectively estimate the system state while avoiding the problem of dimensional explosion, and has strong robustness regarding measurement noise. Thus, this method can establish a basis for system fault diagnosis and fault positioning.

二氧化碳去除系统是控制长期载人航天器中CO ₂质量浓度的最关键系统。为了确保将机舱中的CO ₂质量浓度控制在允许范围内，需要实时估计CO ₂去除系统的状态。本文首先建立了描述实际系统条件的数学模型，然后提出了基于Galerkin的扩展卡尔曼滤波算法来估计CO ₂的状态。。该方法采用Galerkin逼近法将偏微分方程转换为常微分方程，然后利用扩展卡尔曼滤波器进行状态估计。以实际载人航天任务为条件进行了模拟实验。仿真结果表明，该方法可以有效地估计系统状态，同时避免了尺寸爆炸的问题，并且在测量噪声方面具有很强的鲁棒性。因此，该方法可以为系统故障诊断和故障定位奠定基础。