Title: Aerodynamic derivatives identification for ground vehicles in crosswind using neural network and PCA

Authors: Nabilah Ramli, Hishamuddin Jamaluddin, Shuhaimi B. Mansor, Waleed F. Faris

Addresses: Department of Mechanical Engineering, College of Engineering, International Islamic University Malaysia (IIUM), Jalan Gombak, Kuala Lumpur 53100, Malaysia. ' Fakulti Kejuruteraan Mekanikal, Universiti Teknologi Malaysia, Skudai, Johor 81310, Malaysia. ' Fakulti Kejuruteraan Mekanikal, Universiti Teknologi Malaysia, Skudai, Johor 81310, Malaysia. ' Department of Mechanical Engineering, College of Engineering, International Islamic University Malaysia (IIUM), Jalan Gombak, Kuala Lumpur 53100, Malaysia

Abstract: Principal component analysis (PCA) is employed in this study to reduce the size of the neural network input node. Neural network is used to identify the ground vehicle aerodynamic derivatives based on a recorded simple harmonic motion of a ground vehicle model. The study involves the identification using neural network with and without the input optimisation by PCA. Both studies are compared with the identification results from a conventional method, and it is shown that the neural network can approximate functions based on principal components extracted as well as a full-size neural network can.

Keywords: aerodynamic derivatives cross wind; vehicle stability; PCA; principal component analysis; neural networks; vehicle aerodynamics; harmonic motion; vehicle systems modelling.

DOI: 10.1504/IJVSMT.2010.033731

International Journal of Vehicle Systems Modelling and Testing, 2010 Vol.5 No.1, pp.59 - 71

Received: 07 Aug 2009
Accepted: 08 Dec 2009
Published online: 29 Jun 2010 *

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