Octorotor PMSM 의 상호 다중 모델 필터 기반 고장 검출과 진단

Abstract

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.