基于ATSUKF的飞行器惯性测量单元的故障诊断

Aircraft Inertial Measurement Unit Fault Diagnosis Based on Adaptive Two-Stage UKF

非线性系统存在随机偏差情况下,最优二步无迹卡尔曼滤波(OTSUKF)可以获得系统状态及偏差的最优估计,但是它要求随机偏差被准确地建模,而这在实际情况下很难做到。飞行器是一种典型的非线性系统,将惯性测量单元(IMU)的故障作为一种随机偏差处理,并且采用随机游走模型去描述故障。随机游走模型对故障进行建模的准确程度取决于随机游走模型的协方差与实际情况的匹配程度。基于OTSUKF的IMU故障诊断方法中,随机游走模型的协方差取的是一个常值矩阵,该矩阵的值是根据经验初始化的,但是在实际应用中较难初始化为一个与真实故障相匹配的矩阵。根据新息协方差匹配技术,在线自适应调整随机游走模型的协方差矩阵,提出了自适应二步无迹卡尔曼滤波(ATSUKF),并将该方法应用于飞行器IMU的故障诊断。仿真实验对比了OTSUKF和ATSUKF方法对飞行器IMU的故障诊断的效果,验证了所提出的自适应方法的有效性。

In the case of nonlinear systems with random bias,the Optimal Two-Stage Unscented Kalman Filter(OTSUKF)can obtain the optimal estimation of system state and bias.But it requires random bias to be accurately modeled,while it is always very difficult in actual situation because the aircraft is a typical nonlinear system.In this paper,the faults of the Inertial Measurement Unit(IMU)are treated as a random bias,and the random walk model is used to describe the fault.The accuracy of the random walk model depends on the degree of matching between the covariance of the random walk model and the actual situation.For the IMU fault diagnosis method based on OTSUKF,the covariance of the random walk model is assigned with a constant matrix,and the value of the matrix is initialized empirically.It is very difficult to select a matching matrix in practical applications.For this problem,in this paper,the covariance matrix of the random walk model is adaptively adjusted online based on the innovation covariance matching technique,and an adaptive Two-Stage Unscented Kalman Filter(ATSUKF)is proposed to solve the fault diagnosis problem of the IMU.The simulation experiment compares the IMU fault diagnosis performance of OTSUKF and ATSUKF,and verifies the effectiveness of the proposed adaptive method.