Nonlinear aircraft attitude and heading reference system failure detection and identification

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Abstract

Using the kinematic model of the aircraft for sensor Fault Detection and Identification (FDI) can reduce the influence of model uncertainties. Many papers have used this method to detect the faults in the aircraft Air Data Sensors (ADSs). However, the Attitude Heading and Reference System (AHRS) is assumed to be fault-free in previous studies. In this paper, both the ADS and AHRS faults are considered. The kinematic model including the ADS and AHRS faults is given. An Adaptive Three-Step Unscented Kalman Filter (ATS-UKF) is designed to deal with the sensor FDI problem. The FDI performance of the ATS-UKF is validated using both simulated aircraft data where no model uncertainties are included and real flight test data where model uncertainties and varying winds are present. Both the validations use different fault scenarios, which contains multiple and simultaneous faults. The results demonstrate that the ATS-UKF is able to detect, isolate and estimate the faults in the ADSs and AHRS.

Original languageEnglish
Title of host publicationAIAA Atmospheric Flight Mechanics Conference
PublisherAmerican Institute of Aeronautics and Astronautics Inc. (AIAA)
Number of pages18
ISBN (Print)9781624103902
DOIs
Publication statusPublished - 2016
EventAIAA Atmospheric Flight Mechanics Conference, 2016 - Reston, San Diego, United States
Duration: 4 Jan 20168 Jan 2016
https://doi.org/10.2514/MAFM16

Conference

ConferenceAIAA Atmospheric Flight Mechanics Conference, 2016
CountryUnited States
CitySan Diego
Period4/01/168/01/16
Internet address

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  • Cite this

    Lu, P., van Kampen, E., de Visser, C. C., & Chu, Q. P. (2016). Nonlinear aircraft attitude and heading reference system failure detection and identification. In AIAA Atmospheric Flight Mechanics Conference [AIAA 2016-1755] American Institute of Aeronautics and Astronautics Inc. (AIAA). https://doi.org/10.2514/6.2016-1755