부산대학교 도서관

Regarding the mechanical engineering area, over the last 40 years a lot of effort has been undertaken to find very exact descriptions for the dynamic behavior of road vehicles based on mathematical models. All those models include certain parameter values which may be taken from data sheets or which have to be measured or determined by real driving tests. Using these physical models for vehicle simulation purposes, the problem arises, that some of the model parameters are time-variant. They vary over a smaller or larger time period, e.g. due to aging, different vehicle loads or changing environmental conditions like a transition from dry to wet or icy road. Parameter variations lead to systematic modeling errors which makes simulation results turn out incorrect. To overcome that problem, this paper describes the use of hybrid models to reduce modeling errors. Within hybrid models, conventional mathematical process models are combined with adaptive learning structures, e.g. neural networks. In this contribution, an extended radial basis function network called LOLIMOT (local linear model tree) is used to compensate the influences of changing road conditions affecting a vehicle dynamics simulation model.