无人机控制系统传感器故障诊断的方案与仿真

The Study and Simulation of Sensor Fault Diagnosis for an UAV lControl System

应用卡尔曼滤波器对传感器进行故障诊断时,由于输入噪声和测量噪声的统计特性是不确定的,因此难以得到其准确的统计特性先验信息,而采用错误的噪声统计特性会产生滤波误差,甚至使滤波发散,因此该文提出了一种基于Sage-Husa时变噪声统计估计器的自适应卡尔曼滤波器算法,在滤波过程中利用噪声统计估计器对未知的统计特性进行在线估计,并对无人机控制系统的传感器故障进行在线诊断,此方法无须增加硬件余度和其他解析余度,易于实现,可靠性好,检测迅速。仿真表明该方法能够检测出系统故障并进行故障定位。

Because of the uncertainty of measurement noise and input noise, it is difficult to get their exact statistical characteristic when applying Kalman filter to diagnose sensor faults. If the false noise statistical characteristic is used, the filtering may be not convergent. Based on the adaptive Kalman filter with Sage - Husa noise estimator, the paper discusses the fault detection and isolation method for the sensor faults of UAV control system. This method uses noise estimator to get the unknown statistical characteristic and this method is easy because it is not necessary to use hardware redundancy and other analytical redundancy. A simulation example of an UAV control system is given for illustration.