Neural networks-based sensor validation for the flight control system of a B777 research model

Shows the results of the analysis of a scheme for sensor failure, detection, identification and accommodation (SFDIA) using experimental flight data of a research aircraft model. Conventional approaches to the problem are based on observers and Kalman filters while more recent methods are based on neural approximators. The work described in the paper is based on the use of neural networks (NNs) as online learning nonlinear approximators. The performances of two different neural architectures are compared. The first architecture is based on a multi layer perceptron NN trained with the extended backpropagation algorithm. The second architecture is based on a radial basis function (RBF) NN trained with the extended minimal resource allocating networks (EMRAN) algorithms. The experimental data for this study are acquired from the flight-testing of a 1/24th semi-scale B777 research model designed, built, and flown at West Virginia University.