A method to predict vertical vehicle motion caused by track irregularities

Abstract

In this study, the author uses parametric models to predict the track geometry-induced dynamic behavior of a vehicle. The characteristics of vehicle dynamics in these models are directly identified through the spatial as opposed to the frequency domain. One of the former’s merits is that we can identify the characteristics of vehicle dynamics with fewer observed points than when we use spectral analysis, making it easier to obtain fewer data set for identification. Another is that we can determine the parameters to represent dynamic behavior of a vehicle using statistical criteria. With these models, we can predict vertical acceleration of a vehicle and its wheel load as well as estimate track conditions by taking into account both ride comfort and operating safety.

Keywords

Vehicle dynamic response track irregularity system identification ARX model

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References

ORE C116 rp.2. A comparison of theoretical and experimental vehicle behavior using a 2-axled special vehicle, 1972, 10 p.

Ljung

. System identification – theory for the user, 2nd ed. Upper Saddle River, NJ: PTR Prentice Hall, 1999.

Overschee

Moor

. Subspace algorithms for the identification of combined deterministic–stochastic system. Automatica 1994; 30: 75–93.

Akaike H. Information theory and an extension of the maximum likelihood principle. 2nd ed. Akademiai Kiado, Budapest: Inter Symposium on Information Theory, 1973.

Esvelt

. Modern railway track, 2nd ed. The Netherlands: MRT-Productions, 2001, pp. 137–151.