NDLI: Performance evaluation of neural network based approaches for airspeed Sensor Failure Accommodation on a small UAV

Content Provider	IEEE Xplore Digital Library
Author	Gururajan, S. Fravolini, M.L. Haiyang Chao Rhudy, M. Napolitano, M.R.
Copyright Year	2013
Description	Author affiliation: Dept. of Mech. & Aerosp. Eng., West Virginia Univ., Morgantown, WV, USA (Gururajan, S.; Haiyang Chao; Rhudy, M.; Napolitano, M.R.) \|\| DIEI, Univ. of Perugia, Perugia, Italy (Fravolini, M.L.)
Abstract	Traditional approaches to sensor fault tolerance for flight control systems have been based on triple or quadruple physical redundancy. However, recent events have highlighted the criticality of "common mode" failures on the Air Data System (ADS). In fact, since the parameters of flight control laws are typically scheduled as a function of airspeed, incorrect readings from the ADS can lead to potentially catastrophic conditions. In this paper, we describe the evaluation of an analytical redundancy-based approach to the problem of Sensor Failure Accommodation following simulated failures on the ADS of a research UAV, using Artificial Neural Networks (ANNs). Specifically, two different neural networks are evaluated - the Extended Minimal Resource Allocating Network and a Multilayer Feedforward NN. These neural networks are trained and validated using experimental flight data from the WVU YF-22 research aircraft which was designed, manufactured, instrumented, and flight tested by researchers at the Flight Control Systems Laboratory at West Virginia University. The performance of the two approaches is evaluated in terms of the statistics of the tracking error in the estimation of the airspeed, as compared to actual measurements from the ADS, operating under nominal conditions.
Starting Page	603
Ending Page	608
File Size	341929
Page Count	6
File Format	PDF
e-ISBN	9781479909971
DOI	10.1109/MED.2013.6608784
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-06-25
Publisher Place	Greece
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Neurons Redundancy Artificial neural networks Aircraft Standards Aerospace control
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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