# Aircraft Unsteady Aerodynamic Hybrid Modeling Based on State-Space Representation and Neural Network

### Ouyang Guang, Lin Jun, Zhang Ping

#### 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.

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#### Paper Citation

#### in Harvard Style

Guang O., Jun L. and Ping Z. (2016). **Aircraft Unsteady Aerodynamic Hybrid Modeling Based on State-Space Representation and Neural Network** . In *Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,* ISBN 978-989-758-173-1, pages 232-239. DOI: 10.5220/0005691702320239

#### in Bibtex Style

@conference{icpram16,

author={Ouyang Guang and Lin Jun and Zhang Ping},

title={Aircraft Unsteady Aerodynamic Hybrid Modeling Based on State-Space Representation and Neural Network},

booktitle={Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},

year={2016},

pages={232-239},

publisher={SciTePress},

organization={INSTICC},

doi={10.5220/0005691702320239},

isbn={978-989-758-173-1},

}

#### in EndNote Style

TY - CONF

JO - Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,

TI - Aircraft Unsteady Aerodynamic Hybrid Modeling Based on State-Space Representation and Neural Network

SN - 978-989-758-173-1

AU - Guang O.

AU - Jun L.

AU - Ping Z.

PY - 2016

SP - 232

EP - 239

DO - 10.5220/0005691702320239