基于气动参数辨识的飞控系统传感器故障估计

气动参数的不确定性使得飞行器表现出明显的模型时变特点，此类系统的故障诊断问题是一个难点。以无人机纵向运动为研究对象，提出一种基于气动参数辨识和迭代学习的传感器故障估计方案。将增广容积卡尔曼滤波（ACKF）算法用于气动参数估计，实现飞机模型的在线辨识。故障一旦发生，将辨识得到的气动参数用于局部包络建模，并利用迭代学习算法构造传感器故障估计器。此外，为提高故障的迭代收敛速度，提出一种基于扩张状态观测器(ESO)思想的迭代学习算法。故障仿真实验表明了所提方法的可行性和有效性。

Sensor Fault Estimation Method for Flight Control Systems Based on Aerodynamic Parameter Identification

The aircraft model shows obvious time-varying characteristic due to the uncertainty of aerodynamic parameters. The fault diagnosis of the flight control systems is a difficult issue. A sensor fault estimation approach based on aerodynamic parameter identification and iterative learning is proposed by taking the longitudinal motion model of some unmanned aerial vehicle as the study subject. The augmented cubature Kalman filter (ACKF) is used for the aerodynamic parameter estimation so that the system model can be identified online. Once a fault comes up, the currently identified aerodynamic parameters are applied to system modeling in the local flight envelope, and a fault estimator is constructed using the iterative learning algorithm. Furthermore,a novel iterative learning algorithm based on the essence of extended state observer (ESO) is designed to improve the fault estimation speed. The fault simulation experiments are conducted to verify the feasibility and effectiveness of the proposed approach.