This paper studies neural network models of vehicle dynamics. We consider both models with a generic layer architecture and models with specialised topologies that hard-wire physics principles. Network pre-wiring is limited to universal laws; hence it does not limit the network modelling abilities on one side but allows more robust and interpretable models on the other side. Four different network types (with and without pre-wired structure, recursive and non-recursive) are compared for the longitudinal dynamics of a car with gears and two controls (brake and engine). Results show that pre-wiring effectively improves the performance. Non-recursive networks also look to be preferable for several reasons.
Modelling longitudinal vehicle dynamics with neural networks
Vehicle System Dynamics ; 58 , 11 ; 1675-1693
2020-11-01
19 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
Modelling nonlinear vehicle dynamics with neural networks
Tema Archiv | 2010
|Modelling nonlinear vehicle dynamics with neural networks
Kraftfahrwesen | 2010
|Modelling nonlinear vehicle dynamics with neural networks
Online Contents | 2010
|Identification of Micro Air Vehicle Longitudinal Dynamics using Neural Networks: A Comparative Study
Online Contents | 2001
|