Neural Network Tire Force Modeling for Automated Drifting

Autor:	Broadbent, Nicholas Drake, Weber, Trey, Mori, Daiki, Gerdes, J. Christian
Rok vydání:	2024
Předmět:	Electrical Engineering and Systems Science - Systems and Control Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	Automated drifting presents a challenge problem for vehicle control, requiring models and control algorithms that can precisely handle nonlinear, coupled tire forces at the friction limits. We present a neural network architecture for predicting front tire lateral force as a drop-in replacement for physics-based approaches. With a full-scale automated vehicle purpose-built for the drifting application, we deploy these models in a nonlinear model predictive controller tuned for tracking a reference drifting trajectory, for direct comparisons of model performance. The neural network tire model exhibits significantly improved path tracking performance over the brush tire model in cases where front-axle braking force is applied, suggesting the neural network's ability to express previously unmodeled, latent dynamics in the drifting condition. Comment: 16th International Symposium on Advanced Vehicle Control (AVEC). September 2nd-6th, 2024. Milan, Italy
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2407.13760 Zobrazit plný text záznamu View this record from Arxiv