Certification analysis for a model-based UAV fault detection system

Bin Hu, Peter J Seiler Jr

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations


Model-based fault detection algorithms can be used to improve the reliability of unmanned aerial vehicles (UAVs) while still satisfying their restrictive size, power, and weight requirements. However, the use of model-based algorithms introduces new failure modes that do not exist in physically redundant architectures. Hence a certification process is needed for such systems that incorporates analysis tools, high fidelity simulations, and ight test data. This paper focuses on one aspect of such a process: the use of ight test data to validate theoretical analysis results. Specifically, this validation is performed to assess the false alarm probability of a simple, model-based UAV fault detection system. This example highlights the main certification issues that arise due to limited ight data and stringent reliability requirements. In addition, the ight test data shows non-Gaussian statistical behavior that leads to some discrepancies with the analysis results. Further discussions are presented for this observed behavior.

Original languageEnglish (US)
Title of host publicationAIAA Guidance, Navigation, and Control Conference
StatePublished - 2014
EventAIAA Guidance, Navigation, and Control Conference 2014 - SciTech Forum and Exposition 2014 - National Harbor, MD, United States
Duration: Jan 13 2014Jan 17 2014

Publication series

NameAIAA Guidance, Navigation, and Control Conference


OtherAIAA Guidance, Navigation, and Control Conference 2014 - SciTech Forum and Exposition 2014
Country/TerritoryUnited States
CityNational Harbor, MD

Bibliographical note

Funding Information:
The authors would like to thank Brian Taylor and Andrei Dorobantu for providing support with the experimental data and aircraft modeling. This work was partially supported by the National Science Foundation under Grant No. NSF-CMMI-1254129 entitled CAREER: Probabilistic Tools for High Reliability Monitoring and Control of Wind Farms. It was also partially supported the NASA Langley NRA Cooperative Agreement NNX12AM55A entitled Analytical Validation Tools for Safety Critical Systems Under Loss-of-Control Conditions, Dr. Christine Bel-castro technical monitor. Any opinions, findings, and conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the NSF or NASA.

Copyright 2014 Elsevier B.V., All rights reserved.


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