System Identification for Dynamic Modeling of Large Steering Angle Vehicles
Provides improved dynamic modeling for autonomous vehicles with high maneuverability, relevant for educational experimental frameworks.
The paper develops modified bicycle models for vehicles with large steering angles and combines them with parametric and non-parametric identification techniques. Physics-informed neural network models achieve higher accuracy than purely physical models at lower computational cost.
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.