A Novel Data-Driven Modeling and Control Design Method for Autonomous Vehicles

Dániel Fényes, Balázs Németh and Péter Gáspár
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Dániel Fényes: Department of Control for Transportation and Vehicle Systems, Budapest University of Technology and Economics, H-1111 Budapest, Hungary
Balázs Németh: Systems and Control Laboratory, Institute for Computer Science and Control (SZTAKI), H-1111 Budapest, Hungary
Péter Gáspár: Systems and Control Laboratory, Institute for Computer Science and Control (SZTAKI), H-1111 Budapest, Hungary

Energies, 2021, vol. 14, issue 2, 1-16

Abstract: This paper presents a novel modeling method for the control design of autonomous vehicle systems. The goal of the method is to provide a control-oriented model in a predefined Linear Parameter Varying ( LPV ) structure. The scheduling variables of the LPV model through machine-learning-based methods using a big dataset are selected. Moreover, the LPV model parameters through an optimization algorithm are computed, with which accurate fitting on the dataset is achieved. The proposed method is illustrated on the nonlinear modeling of the lateral vehicle dynamics. The resulting LPV -based vehicle model is used for the control design of path following functionality of autonomous vehicles. The effectiveness of the modeling and control design methods through comprehensive simulation examples based on a high-fidelity simulation software are illustrated.

Keywords: LPV control design; machine learning in modeling; vehicle dynamics (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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