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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/8328

Title: Structured learning of a non-linear dynamic system component for vehicle motion simulation
Authors: Best, Matt C.
Newton, Andrew P.
Keywords: Vehicle dynamics and control
Integrated motion control
Modeling and simulation technology
Virtual reality
Issue Date: 2006
Publisher: © Society of Automotive Engineers of Japan (JSAE)
Citation: BEST, M.C. and NEWTON, A.P., 2006. Structured learning of a non-linear dynamic system component for vehicle motion simulation. Proceedings of the 8th International Symposium on Advanced Vehicle Control (AVEC), Taipei, Taiwan, 20th-24th August, pp. 559-564.
Abstract: Here we consider the design of a general subsystem model which is required to operate in series with the known dynamics of a plant or actuator, to achieve a desired overall system response. The example model is used in series with a motion platform to emulate vehicle handling dynamics. The method works in stages, first isolating the required linear response by fitting a frequency response function, then modelling this with a fixed order linear system in modal canonical form. A nonlinear saturation is then optimised for each modal state. The results are demonstrated for simulated and vehicle test data, and these achieve the principal objectives, of low state and parameter order. Some limitations to the method emerge – principally that there remain challenges to extension of the model to multi-input / output operation.
Description: This is a conference paper.
Version: Accepted for publication
URI: https://dspace.lboro.ac.uk/2134/8328
Publisher Link: http://www.jsae.or.jp/index_e.php
ISBN: 109860059470
Appears in Collections:Conference Papers and Contributions (Aeronautical and Automotive Engineering)

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