System Identification for Dynamic Modeling of Large Steering Angle Vehicles

arXiv (Cornell University)

Abstract

This paper presents the modeling of autonomous vehicles with high maneuverability used in an experimental framework for educational purposes. Since standard bicycle models typically neglect wide steering angles, we develop modified planar bicycle models and combine them with both parametric and non-parametric identification techniques that progressively incorporate physical knowledge. The resulting models are systematically compared to evaluate the tradeoff between model accuracy and computational requirements, showing that physics-informed neural network models surpass the purely physical baseline in accuracy at lower computational cost.

Authors 2

1. Simone Baratto Aachen

   RWTH Aachen University · École Polytechnique Fédérale de Lausanne

   Affiliation as printed

   RWTH Aachen University , Aachen , Germany

   École Polytechnique Fédérale de Lausanne , DECODE lab , Lausanne , Switzerland

2. Giancarlo Ferrari‐Trecate Aachen

   RWTH Aachen University · École Polytechnique Fédérale de Lausanne

   Affiliation as printed

   RWTH Aachen University , Aachen , Germany

   École Polytechnique Fédérale de Lausanne , DECODE lab , Lausanne , Switzerland