Neural Network Tire Force Modeling for Automated Drifting
This addresses the challenge of precise vehicle control at friction limits for automated drifting, though it appears incremental as it replaces one model component in a specific application.
The paper tackled the problem of modeling nonlinear tire forces for automated drifting by developing a neural network architecture to predict front tire lateral force, which when deployed in a nonlinear model predictive controller showed significantly improved path tracking performance over a physics-based brush tire model, particularly when front-axle braking force was applied.
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.