https://orcid.org/https://orcid.org/0000-0001-5601-9059
https://orcid.org/https://orcid.org/0000-0002-0476-4118
https://orcid.org/https://orcid.org/0000-0002-9807-5056
https://orcid.org/https://orcid.org/0000-0003-2644-7487
https://orcid.org/https://orcid.org/0000-0001-5829-2323
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.
Neural Network Tire Force Modeling for Automated Drifting
Lect.Notes Mechanical Engineering
Advanced Vehicle Control Symposium ; 2024 ; Milan, Italy September 01, 2024 - September 05, 2024
2024-10-04
7 pages
Article/Chapter (Book)
Electronic Resource
English
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