Premium
On‐line learning neural networks for sensor validation for the flight control system of a B777 research scale model
Author(s) -
Campa Giampiero,
Fravolini Mario L.,
Seanor Brad,
Napolitano Marcello R.,
Gobbo Diego Del,
Yu Gu,
Gururajan Srikanth
Publication year - 2002
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.728
Subject(s) - artificial neural network , computer science , perceptron , radial basis function , artificial intelligence , estimator , nonlinear system , kalman filter , machine learning , extended kalman filter , multilayer perceptron , control engineering , engineering , statistics , physics , mathematics , quantum mechanics
This paper focuses on the analysis of a scheme for sensor failure, detection, identification and accommodation (SFDIA) using experimental flight data of a research aircraft model. Recent technical literature has shown the advantages of time‐varying estimators and/or approximators. Conventional approaches are based on different versions of observers and Kalman filters while more recent methods are based on different approximators based on neural networks (NNs). The approach proposed in the paper is based on the use of on‐line learning nonlinear neural approximators. The characteristics of three different neural architectures were compared through different sensor failures. The first architecture is based on a multi layer perceptron (MLP) NN trained with the extended back propagation algorithm (EBPA). The second and third architectures are based on a radial basis function (RBF) NN trained with the minimal resource allocating network (MRAN) and extended‐MRAN (EMRAN). The MRAN and EMRAN algorithms have recently been developed for RBF networks and have shown remarkable learning capabilities at a fraction of the memory requirements and computational effort typically associated with conventional RBF NNs. The experimental data for this study are flight data acquired from the flight‐testing of a $$1 \over 24$$ th semi‐scale B777 research model designed, built, and flown at West Virginia University (WVU). Copyright © 2002 John Wiley & Sons, Ltd.