基于迭代学习-未知输入观测器的卫星姿控系统鲁棒故障重构

针对卫星姿控系统出现执行机构故障，提出一种基于迭代学习-未知输入观测器(iterative learning unknown input observer, IL-UIO)的鲁棒故障重构方法。首先,考虑卫星出现空间干扰力矩、模型不确定性以及陀螺漂移，建立小角度机动时的非线性姿控系统模型。其次,采用UIO干扰解耦原理和H_∞控制思想，设计IL-UIO估计卫星姿态欧拉角和角速度的同时，利用IL算法实现执行机构鲁棒故障重构。并利用Lyapunov稳定性理论证明了IL-UIO稳定性和动态故障偏差最终有界, 通过线性矩阵不等式(linear matrix inequaliry, LMI)工具箱求解了观测器部分参数矩阵。最后，建立卫星闭环姿控系统并进行仿真，仿真结果验证了此方法的有效性。

Robust fault reconstruction method for satellite attitude control system based on iterative learning-unknown input observer

A robust fault reconstruction method based on the iterative learning-unknown input observer(IL-UIO) is proposed for actuator fault in satellite attitude control systems(ACS).Firstly,considering space disturbance torque,model uncertainties and gyro drift,the nonlinear model of attitude control is established when a three-axis stability satellite runs in a small angle maneuver.Secondly,based on the disturbance decoupling principle of UIO and H∞ control theory,the IL-UIO is designed to estimate attitude Euler angles and angular velocities,and the IL algorithm is used to achieve actuator robust fault reconstruction.Using the Lyapunov stability theorem,the stability of IL-UIO and the ultimate boundedness of dynamic fault errors are proved,the parameter matrixes of IL-UIO are solved effectively in terms of linear matrix inequality(LMI) toolbox.Finally,mathematical simulation is performed to validate the solution in satellite closed-loop ACS,and simulation results demonstrate the effectiveness of the proposed algorithm.