Neural Network Tire Force Modeling for Automated Drifting
<|reference_start|>Neural Network Tire Force Modeling for Automated Drifting: 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.<|reference_end|>
@article{broadbent2024neural,
    title={Neural Network Tire Force Modeling for Automated Drifting},
    author={Nicholas Drake Broadbent, Trey Weber, Daiki Mori, and J. Christian
  Gerdes},
    journal={arXiv preprint arXiv:2407.13760},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.13760},
    primaryClass={eess.SY cs.AI cs.SY}
}