Authors: Nicholas Drake Broadbent, Trey Weber, Daiki Mori, J. Christian Gerdes
Abstract: 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.
Source: http://arxiv.org/abs/2407.13760v1
