Fault Detection and Diagnosis of Octorotor PMSMs Using an Interacting Multiple Model Filter

被引：0
|
作者：
Jeong C. [1 ]
Park C. [1 ]
Kang C.M. [1 ]
机构：
[1] Department of Electrical Engineering, Incheon National University
来源：
Journal of Institute of Control, Robotics and Systems | 2022年 / 28卷 / 02期
关键词：
Extended Kalman filter (EKF); Fault detection and diagnosis (FDD); Interacting multiple model (IMM) filter; Permanent magnet synchronous motor (PMSM); Unmanned aerial vehicle (UAV);
D O I：
10.5302/J.ICROS.2022.21.0230
中图分类号：
学科分类号：
摘要：
Fault detection and diagnosis (FDD) is an essential algorithm applied to unmanned aerial vehicles (UAVs) performing low-level to high-level missions. A UAV consists of many mechanical and electrical components, and the failure of one component may affect others, potentially leading to fatal accidents. Herein, we propose a multiple filter-based FDD method that can classify component faults. An interacting multiple model (IMM) filter is combined with several filter algorithms, such as Kalman filter and extended Kalman filter to form a single multiple-filter structure. The suitability of the model can then be evaluated using the likelihood function and mode probability. The highest mode probability in the fault model suggests the existence of a fault. Thus, it is possible to identify the fault type. Because the IMM-based FDD method stochastically estimates the state of the system, it can flexibly cope with different fault situations. The proposed FDD method was applied to the permanent magnet synchronous motor (PMSM) of a UAV and validated via MATLAB/Simulink. © ICROS 2022.
引用
收藏
页码：180 / 190
页数：10
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