# UKF-Based State Estimation for Electrolytic Oxygen Generation System of Space Station
## 1 Introduction
Given the backgrounds of irregular connection of multi-cabin sections, the large space capacity, and the uncertain metabolic activities of many astronauts, the O2 concentration in the capsule is a slowly changing parameter, the response speed of which is very slow [23]. Under such conditions, the concentration of hydrogen and oxygen at the outlet of the EOGS system can be used as direct indicator parameters for online monitoring. The reason is that the concentration of hydrogen and oxygen at the outlet are parameters that can be directly selected. However, electrolytic core, as the core equipment of the electrolytic oxygen system, because of its complicated structure and small space, the working condition inside of which is known with difficulty. Many parameters of electrolytic core, such as the direct oxygen production amount and the working efficiency, cannot be directly measured. If accurate location of the system faults is required, especially the faults that occur inside the electrolytic core, the limited data measured by the sensor will be insufficient, such as the total pressure in the cabin and the partial pressure of oxygen in the cabin. In order to solve this issue, a system state observer needs to be designed. As one kind of improved Kalman filer, Unscented Kalman filter (UKF) can be widely used in the design of the state observer because it can approximate the probability density distribution of nonlinear state variables. Instead of approximating the nonlinear function, it uses a series of deterministic samples to approximate the posterior probability density of the state variables. Compared with extended Kalman filter (EKF), neither the derivative calculation of Jacobian matrix is required [24; 25], nor the higher-order terms are ignored during linearization, and thus the calculation accuracy of nonlinear distribution statistics is higher than EKF [26; 27; 28]. In addition, [PERSON] et al. studied the health management of metal roof structure [29], [PERSON] et al. conducted multi-factor analytic hierarchy process [30], and [PERSON] et al. carried out multi-objective optimization for power system [31], all of these researches have the important reference significance for health management of the space station system. In this paper, according to the operating conditions of the actual system, and the operating mechanism and conditions of the key equipment and the entire system, a mathematical model that can describe the dynamic changes of the parameters is established, and then the nonlinear state observer of the system is designed by UKF to monitor the state of the system and diagnose fault. The most important task is to perform condition monitoring and fault diagnosis for the electrolytic core. It is also under consideration to locate and diagnose the faults of other main components including pipelines and steam separators. Thus, various abnormalities or failures of the EOGS system can be timely and accurately diagnosed, prevented and eliminated. Reliability, safety, and effectiveness of equipment operation can be improved. In this way, the possible failure loss of the system can be reduced to the lowest level. The structure of this paper is as follow: firstly, the function, working process and working principle of the EOGS in the physical and chemical regenerative environment control and life support system of the space station are described in detail. Then, according to the previous correlation analysis, the kinetic model of oxygen production system is established. Then, a state observer based on UKF algorithm is designed. At last, a simulation experiment is designed with one more astronaut than the Chinese space laboratory TG-2. The experimental results show the accuracy of the model is good and the effectiveness of the state estimation scheme is valid.