On-line Identification of longitudinal Aircraft Dynamics Using Extended Neural Networks
Bayoumy, A., Abdel-Ghany, F., Ibrahim, I. (1999). On-line Identification of longitudinal Aircraft Dynamics Using Extended Neural Networks. EKB Journal Management System, 8(ASAT Conference, 4-6 May 1999), 1-10. doi: 10.21608/asat.1999.24865
A. M. Bayoumy; F. I. Abdel-Ghany; I. M. Ibrahim. "On-line Identification of longitudinal Aircraft Dynamics Using Extended Neural Networks". EKB Journal Management System, 8, ASAT Conference, 4-6 May 1999, 1999, 1-10. doi: 10.21608/asat.1999.24865
Bayoumy, A., Abdel-Ghany, F., Ibrahim, I. (1999). 'On-line Identification of longitudinal Aircraft Dynamics Using Extended Neural Networks', EKB Journal Management System, 8(ASAT Conference, 4-6 May 1999), pp. 1-10. doi: 10.21608/asat.1999.24865
Bayoumy, A., Abdel-Ghany, F., Ibrahim, I. On-line Identification of longitudinal Aircraft Dynamics Using Extended Neural Networks. EKB Journal Management System, 1999; 8(ASAT Conference, 4-6 May 1999): 1-10. doi: 10.21608/asat.1999.24865

On-line Identification of longitudinal Aircraft Dynamics Using Extended Neural Networks

International Conference on Aerospace Sciences and Aviation Technology
Article 6, Volume 8, ASAT Conference, 4-6 May 1999, May 1999, Page 1-10  XML PDF (1.87 MB)
Document Type: Original Article
DOI: 10.21608/asat.1999.24865
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Authors
A. M. Bayoumy1; F. I. Abdel-Ghany2; I. M. Ibrahim3
1Eng. Amgad M. Bayoumy, Military Technical College (MTC), Cairo, Egypt.
2Assoc.Prof. Fawzy Ibrahim AbdelGhany, Chairman of Radar and Guidance Department, MTC, Cairo, Egypt.
3Prof. Ibrahim Mansour Ibrahim, Chairman of Design Center, Ministry of Military Production, Cairo, Egypt Military Technical College Kobry Elkubbah, Cairo, Egypt.
Abstract
In this paper Artificial Neural Networks (ANN) approach is used to identify the longitudinal aircraft dynamics online. Memory Neuron NN (MNN) is used here as a model to perform online non-parametric identification of aircraft dynamics. The aircraft longitudinal motion is modeled as a linear system. The input and output of the aircraft model are used as training pair to the NN model. NN is extended by adding two more parameters, the maximum input and maximum output vectors, to learn the changes in I/O bounds dynamically. After the training phase the NN output shows good agreement with the aircraft output for the same input.
Keywords
Aircraft; Neural Networks; online Identification
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