Neural Networks-Based Sensor Validation for the Flight Control System of a B777 Research Model

This paper 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 this paper is based on the use of neural networks (NNs) as on-line learning non-linear approximators. The performances of two different neural architectures were compared. The first architecture is based on a Multi Layer Perceptron (MLP) NN trained with the Extended Back Propagation algorithm (EBPA). The second architecture is based on a Radial Basis Function (RBF) NN trained with the Extended-MRAN (EMRAN) algorithms. The EMRAN algorithm is a training algorithm recently developed for RBF networks which has shown remarkable learning capabilities at a fraction of the memory requirements and computational effort typically associated with RBF NNs. 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 (WVU). List of Acronyms EBPA Extended Back Propagation Algorithm EMRAN Extended Minimal Resource Allocating Networks MLP Multi Layer Perceptrion MRAN Minimal Resource Allocating Network NN Neural Network RAN Resource Allocating Networks RBF Radial Basis Function SFDIA Sensor Failure Detection, Identification, and Accommodation