Authors:
Ouyang Guang
;
Lin Jun
and
Zhang Ping
Affiliation:
Beihang University, China
Keyword(s):
Unsteady aerodynamics, State-Space representation, Back-propagation neural network, Parameter identification, Model optimization.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Hybrid Learning Algorithms
;
Methodologies and Methods
;
Model Selection
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
This paper proposes a hybrid model which combines state-space representation and back-propagation neural network to describe the aircraft unsteady aerodynamic characteristics. Firstly, the state-space model is analysed and evaluated using wind-tunnel experimental data. Subsequently, back-propagation neural network is introduced and combined with state-space representation to form a hybrid model. In this hybrid model, the separation point model in state-space representation is reserved to describe the time delay of the unsteady aerodynamic responses, while the conventional polynomial model is replaced by back-propagation neural network to improve accuracy and universality. Finally, lift coefficient and pitch moment coefficient data from the wind-tunnel experiments are used to estimate the hybrid model. With high similarity to the wind-tunnel data, the hybrid model presented in this paper is proved to be accurate and effective for aircraft unsteady aerodynamic modeling.