Neural Network Tire Force Modeling for Automated Drifting

• URL: http://arxiv.org/abs/2407.13760v1
• Date: Thu, 18 Jul 2024 17:58:01 GMT
• Title: Neural Network Tire Force Modeling for Automated Drifting
• Authors: Nicholas Drake Broadbent, Trey Weber, Daiki Mori, J. Christian Gerdes,
• Abstract summary: We present a neural network architecture for predicting front tire lateral force as a drop-in replacement for physics-based approaches. We deploy these models in a nonlinear model predictive controller tuned for tracking a reference drifting trajectory.
• Score: 0.2999888908665658
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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