电光与控制, 2020, 27(5): 80, 网络出版: 2020-05-01
Airborne Sensor Fault Diagnosis Based on Model Parameter Identification
南京航空航天大学飞行控制研究所, 南京 211106
机载传感器 飞行控制系统 解析模型 故障诊断 联合自适应调节 airborne sensor flight control system analytical model fault diagnosis joint adaptive regulation
机载传感器在飞行控制系统获取飞行状态, 进行内、外回路控制律解算等过程中发挥着至关重要的作用。除了通过增加硬件余度提高系统可靠性外, 还可以增加模型的解析余度提高系统容错能力。基于无人机的气动参数对飞行控制系统进行故障建模, 设计一种故障状态最优估计器, 结合广义卡尔曼滤波方法提出了一种改进的残差检测算法进行在线学习并估计评价系统的故障状态。并且, 在基于周期时间和残差值联合表决的条件下设计了自适应参考模型调节律对解析模型进行实时对比和修正, 减小系统干扰所带来的不确定性误差影响。通过仿真试验验证了所提出方法的可行性与有效性。
Airborne sensors play an important role for the flight control system in the processes of flight status acquiring and control law solving of internal and external loops.In addition to increasing the hardware redundancy to improve the system reliability, it can also increase the analytical redundancy of the model to improve the system fault tolerance.In this paper, the flight control system is modeled and analyzed based on the aerodynamic parameters of UAV, and an optimal fault state estimator is designed.An improved residual detection method is proposed on the basis of Kalman-Bussy filter, which is used to online learn and estimate the fault state of the system.Furthermore, under the condition of joint voting based on periodic time and residual value, an adaptive reference model regulation law is designed to compare and modify the analytical model in real time, so as to reduce the influence of uncertainties caused by system interference.The simulation results show that the proposed method is feasible and effective.