Volume 15, Issue 4 (Journal of Control, V.15, N.4 Winter 2022)                   JoC 2022, 15(4): 71-83 | Back to browse issues page

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Hadidi M, Kargar S M. UAV attitude Sensor Fault Detection Based On Fuzzy Logic and by Neural Network Model Identification. JoC. 2022; 15 (4) :71-83
URL: http://joc.kntu.ac.ir/article-1-743-en.html
1- Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
2- Smart Microgrid Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran
Abstract:   (2551 Views)
Fault detection has always been important in aviation systems to prevent many accidents. This process is possible in different ways. In this paper, we first identify the longitudinal axis plane model using neural network approach. Then based on the obtained model and using fuzzy logic, the aircraft status sensor fault detection unit was designed. The simulation results show that the fault detection system is able to work well, with additional alarms averaging 1 alert per 4-hour flight and miss alert rates averaging 1 alert per 2 hours. The results are confirmed by the experts from the UAV system.
Full-Text [PDF 1038 kb]   (36 Downloads)    
Type of Article: Research paper | Subject: Special
Received: 2020/03/1 | Accepted: 2020/12/10 | ePublished ahead of print: 2021/02/18

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