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作动器是无人机的关键执行机构,针对作动器卡死、增益损失、偏差等故障问题,采用检测滤波器和卡尔曼滤波相结合的方法进行故障检测和故障参数估计:使用检测滤波器输出带有作动器故障信息的残差向量,并利用阈值检测和残差方向特性检测和隔离故障;在得到故障警报后使用卡尔曼滤波方法对故障参数进行在线估计,得出故障的具体性质和程度;针对不同的故障形式,采用控制命令补偿或重构的方法进行容错控制。基于X型尾翼无人机的转弯速率模型进行仿真试验,结果验证该方法有效可行,能够实现较快的故障诊断,容错策略可以较好的恢复系统性能。
Actuators are the key actuators of UAV. Fault detection and fault parameter estimation are based on the combination of detection filter and Kalman filter for jamming, gain loss and deviation of actuator. The device outputs the residual vector with the actuator fault information and detects and isolates the fault using the threshold detection and residual directional characteristics. After obtaining the fault alarm, the fault parameters are estimated online using the Kalman filter method to obtain the specific fault Nature and extent of the fault; for different forms of failure, the method of control command compensation or reconstruction fault tolerance control. The simulation results show that the proposed method is effective and feasible, and can achieve faster fault diagnosis. The fault tolerance strategy can recover the system performance better.