基于L2范数最小估计的无人机飞控系统故障检测

Fault detection of unmanned aerial vehicle flight control system based on optimal estimation of the L2-norm

为了实现无人机飞行控制系统的快速在线故障检测,提出一种基于L2范数最小估计的无人机非线性飞行控制系统快速故障检测方法。建立无人机飞行控制系统的非线性故障模型,并将未知输入的L2范数最小估计值作为残差评价函数,对系统故障进行检测。在针对线性离散时变系统故障检测方法研究的基础上,利用Krein空间投影实现残差评价函数的递推计算以减小故障检测计算量。以无人机升降舵及速率陀螺故障检测为例,对算法进行仿真试验验证。试验结果表明:该方法可以快速有效的实现无人机飞行控制系统故障检测,为无人机的安全飞行提供可靠的保障。

In order to realize the rapid online failt detection of unmanned aerial vehicle （UAV） flight control system, a fault detection approach based on optimal estimation of the L2-norm was proposed to the fault detection （FD） of UAV nonlinear flight control system. The nonlinear fault model of UAV flight control system was established, and an optimal estimation of the L2-norm of the unknowninputs was found to be the evaluation function for FD. On the foundation of the approach for linear discrete time-varying systems, the projection in Krein space was applied to calculate the evaluation function recursively, and thus the heavy online computational burden could be solved. The FD for UAV elevator and rate gyros was taken as an example to demonstrate the effectiveness of the proposed method. The results showed that the faults of the UAV flight control system could be detected rapidly through the proposed approach, and the safety of UVA could be guaranteed reliably.