Galerkin-based extended Kalman filter with application to CO₂ removal system

Abstract

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.

Keywords

carbon dioxide removal system / Galerkin / infinite nonlinear filter / extend Kalman filter

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