Hybrid Fault Detection and Isolation Method for Inertial Sensors Using Unscented Kalman Filter

In two-degree of freedom(TDOF) inertial sensors, two axes are mechanically correlated with each other. Fault source of one axis sensor may affect the other axis sensor, and therefore multiple fault detection and isolation(FDI) technique is required. Conventional FDI techniques us ing hardware redundancy need four TDOF inertial sensors for FDI. In this study, three TDOF inertial sensor redudancy case is considered, where conventional FDI technique can detect the fault, but cannot isolate the fault sensor. Hybrid FDI technique is proposed to solve this problem. Hybrid FDI technique utilizes the analytic redundancy by utilizing the unscented kalman filter as well as hardware redundancy for FDI. To verify the effectiveness of the proposed FDI technique, numerical simulations are performed using six degree of freedom nonlinear aircrft dynamics.본 연구는 과학기술부 과학기술부 국가지정연구실사업 (과제번호 M1-0318-00-0028)의 지원으로 연구되었습니다

Reinforcement Learning-Based Optimal Flat Spin Recovery for Unmanned Aerial Vehicle

OAIID:RECH_ACHV_DSTSH_NO:T201702700RECH_ACHV_FG:RR00200001ADJUST_YN:EMP_ID:A000978CITE_RATE:1.856FILENAME:김유단_국제논문_201704_김동해.pdfDEPT_NM:기계항공공학부EMAIL:[email protected]_YN:YFILEURL:https://srnd.snu.ac.kr/eXrepEIR/fws/file/bce8ed5a-91c6-47f8-b117-b10123dd645a/linkN