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Research Papers

# Towards a Generic Vehicle Model

[+] Author and Article Information
Lars Mikelsons1

Institute for Mechatronics and System Dynamics, University of Duisburg-Essen, 47057 Duisburg, Germanymikelsons@imech.de

Thorsten Brandt

Institute for Mechatronics and System Dynamics, University of Duisburg-Essen, 47057 Duisburg, Germanybrandt@imech.de

1

Corresponding author.

J. Comput. Nonlinear Dynam 7(2), 021013 (Jan 10, 2012) (10 pages) doi:10.1115/1.4005438 History: Received June 09, 2011; Accepted November 06, 2011; Revised November 06, 2011; Published January 10, 2012; Online January 10, 2012

## Abstract

Real-time control often requires process models that can be solved adequately fast on prescribed control hardware. One class of applications is model predictive control, which can be considered as one of the most powerful control techniques. However, for real-time control of systems with fast dynamics such as vehicles, appropriate models are necessary. Thus, on the one hand, the design process requires models of adequate complexity to cover all relevant effects, and, on the other hand, real-time control requires models with a limited number of computations. In this contribution, an approach for a generation of generic models of the dynamic of technical systems is presented. In this context generic is twofold. First, the model is dimensionless. In particular, the model is robust with respect to variations of the dimensionless parameters and can be easily projected onto a large variety of geometries. Second, equation-based reduction techniques are used to limit the number of necessary operations for a simulation. Hence, the models can be reduced for given controller hardware. As an application example, a standard vehicle model with nonlinear tire characteristics is used. Here the equation-based reduction techniques lead to a reduction of the required computations by a factor of five. In this case the maximum error compared to the reference model is smaller than $5%$.

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## Figures

Figure 1

Scheme of the dimensional set

Figure 2

Single track model

Figure 3

Distribution of π3 for the complete data set

Figure 4

Distribution of π3 for the reduced data set

Figure 5

Dimensionless longitudinal velocity of the original and the reduced model

Figure 6

Dimensionless yaw rate of the original and the reduced model

Figure 7

Dimensionless lateral velocity of the original and the reduced model

Figure 8

Dimensionless longitudinal velocity, lateral velocity, and the yaw rate of the original and the reduced model

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