Abstract
The system identification is a broad area of research in various fields of engineering. Among them, our concern is to identify the aircraft dynamics by means of the measured motion and control variables using a new approach which is based on the support vector machine (SVM) regression. Due to the computational complexity of SVM, it is suggested to adopt the advanced version of SVM i.e. least square support vector machine (LSSVM) to be used for system identification. LSSVM regression is a network-based approach which requires a user defined kernel function and a set of input-output data for its training before the prediction phase like a neural-network (NN) based procedure. In this paper, LSSVM regression has been used to identify the non-linear dynamics of aircraft using real flight data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Zadeh, L.A.: From circuit theory to system theory. Proc. IRE 50, 856–865 (1962)
Hamel, P.G., Jategaonkar, R.V.: The evolution of flight vehicle system identification. AGARD, DLR Germany, 8–10 May 1995
Jategaonkar, R.V.: Flight Vehicle System Identification: A Time Domain Methodology. AIAA Progress in Aeronautics and Astronautics, vol. 216. AIAA, Weinheim (2006)
Klein, V., Morelli, E.: Aircraft System Identification: Theory and Practice. AIAA Education Series Inc., Reston (2006)
Goman, M., Khrabrov, A.: State-space representation of aerodynamic characteristics of an aircraft at high angles of attack. J. Aircr. 31(5), 1109–1115 (1994)
Hornik, K., Stinchcombe, M., White, H.: Multilayer feedforward networks are universal approximators. Neural Netw. 2, 359–366 (1989)
Hassoun, M.H.: Fundamental of Artificial Neural Networks. The MIT Press, Cambridge (1995)
Haykins, S.: Neural Networks: A Comprehensive Foundation. McMaster University, Macmillan College Publishing Company, New York (1994)
Raisinghani, S.C., Ghosh, A.K., Kalra, P.K.: Two new techniques for parameter estimation using neural networks. Aeronaut. J. 102(1011), 25–29 (1998). UK
Singh, S., Ghosh, A.K.: Estimation of lateral-directional parameters using neural networks based modified delta method. Aeronaut. J. 111(1124), 659–667 (2007). UK
Peyada, N.K., Ghosh, A.K.: Aircraft parameter estimation using new filtering technique based on neural network and Gauss-Newton method. Aeronaut. J. 113(1142), 243–252 (2009)
Kumar, R., Ghosh, A.K.: Nonlinear longitudinal aerodynamic modeling using neural Gauss-Newton method. J. Aircr. 48(5), 1809–1812 (2011). AIAA, USA
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, Berlin (1995)
Suykens, J.A.K., Gestel, T.V., Brabanter, J.D., Moor, B.D., Vandewalle, J.: Least Squares Support Vector Machines. World Scientific Publishing Co., Singapore (2002)
Wang, Q., Qian, W., He, K.: Unsteady aerodynamic modelling at high angles of attack using support vector machines. Chin. J. Aeronaut. 28(3), 659–668 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Verma, H.O., Peyada, N.K. (2017). Modelling of Aircraft’s Dynamics Using Least Square Support Vector Machine Regression. In: Giri, D., Mohapatra, R., Begehr, H., Obaidat, M. (eds) Mathematics and Computing. ICMC 2017. Communications in Computer and Information Science, vol 655. Springer, Singapore. https://doi.org/10.1007/978-981-10-4642-1_12
Download citation
DOI: https://doi.org/10.1007/978-981-10-4642-1_12
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-4641-4
Online ISBN: 978-981-10-4642-1
eBook Packages: Computer ScienceComputer Science (R0)