Authors:
Sreenatha G. Anavatti
1
;
Choi J. Young
2
and
Francois Pischery
3
Affiliations:
1
School of Aerospace, Civil and Mechanical Engineering, University of New South Wales at ADFA, Australia
;
2
School of Electrical Engineering and Computer Science, Seoul National University, Korea, Republic of
;
3
Laboratoire d’Automatique, Industrielle Institut National des Sciences Appliquees, de Lyon, France
Keyword(s):
Auto-landing, Robust Control, Neural Network, Aircraft Dynamics
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Enterprise Information Systems
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neural Network Software and Applications
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
Generalization by the Neural Networks is an added advantage that can provide very good robustness and disturbance rejection properties. By providing a sufficient number of training samples (inputs and their corresponding outputs), a network can deal with some inputs it has never seen before. This ability makes them very interesting for control applications because not only they can learn complicated control functions but they are able to respond to changing or unexpected environments. Aircraft landing system provides one such scenario wherein the flight conditions change quite dramatically over the path of descent. The present work discusses the training of a neural network to imitate a robust controller for auto-landing of an aircraft. The comparisons with the robust controller indicate the additional advantages of the neural network