This paper addresses the study of observer-based two-stage extended Kalman filter (TSEKF) estimation problem for the satellite attitude control system (ACS) in the presence of unknown time-varying actuator faults. In the traditional TSEKF methods, the considered faults always refers to constant signal in the propagation of the filter estimation, even though time-varying faults are taken into account in simulation demonstrations. In order to promote the accuracy of the TSEKF algorithm, a nonlinear observer is designed to obtain the fault dynamics and the state estimation with consideration of the nonlinear nature of the satellite ACS, and its estimation results are treated as exogenous signals used for linearizing the nonlinear ACS model. Then based on the observed fault information and the linearized ACS model, the TSEKF estimator is designed to obtain the exogenous filtering scheme, which can simultaneously reconstruct the state and faults accurately. Finally, by using the so called two-stage exogenous Kalman filter (TSXKF), simulation results show that when a time-varying fault occurs, more ideal estimation results can be obtained than those of TSEKF and better dynamic performance can be achieved than that of nonlinear observers.

本文针对存在未知时变执行器故障的卫星姿态控制系统（ACS）的基于观测器的两级扩展卡尔曼滤波器（TSEKF）估计问题进行了研究。在传统的TSEKF方法中，即使在仿真演示中考虑了时变故障，在滤波器估计的传播过程中，所考虑的故障也始终指的是恒定信号。为了提高TSEKF算法的精度，设计了一个非线性观测器，考虑了卫星ACS的非线性特性，获得了故障动态和状态估计，并将其估计结果作为用于线性化非线性的外源信号处理。 ACS模型。然后根据观察到的故障信息和线性化ACS模型，TSEKF估计器旨在获得外源滤波方案，该方案可以同时准确地重建状态和故障。最后，通过使用所谓的两阶段外源卡尔曼滤波器（TSXKF），仿真结果表明，当发生时变故障时，可以获得比TSEKF更好的估计结果，并且可以获得比TSEKF更好的动态性能。非线性观察者。