NDLI: Aircraft sensor estimation for fault tolerant flight control system using fully connected cascade neural network

Content Provider	IEEE Xplore Digital Library
Author	Hussain, S. Mokhtar, M. Howe, J.M.
Copyright Year	2013
Description	Author affiliation: Sch. of Comput., Univ. of Central Lancashire (UCLan), Preston, UK (Hussain, S.; Mokhtar, M.; Howe, J.M.)
Abstract	Flight control systems that are tolerant to failures can increase the endurance of an aircraft in case of a failure. The two major types of failure are sensor and actuator failures. This paper focuses on the failure of the gyro sensors in an aircraft. The neuron by neuron (NBN) learning algorithm, which is an improved version of the Levenberg-Marquardt (LM) algorithm, is combined with the fully connected cascade (FCC) neural network architecture to estimate an aircraft's sensor measurements. Compared to other neural networks and learning algorithms, this combination can produce good sensor estimates with relatively few neurons. The estimators are developed and evaluated using flight data collected from the X-Plane flight simulator. The developed sensor estimators can replicate a sensor's measurements with as little as 2 neurons. The results reflect the combined power of the NBN algorithm and the FCC neural network architecture.
Starting Page	1
Ending Page	8
File Size	1329980
Page Count	8
File Format	PDF
ISSN	21614407
e-ISBN	9781467361293
DOI	10.1109/IJCNN.2013.6706763
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-08-04
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neurons Biological neural networks Aircraft Training FCC Jacobian matrices Vectors
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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