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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2407.13760 |
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| _version_ | 1866917727075041280 |
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| author | Broadbent, Nicholas Drake Weber, Trey Mori, Daiki Gerdes, J. Christian |
| author_facet | Broadbent, Nicholas Drake Weber, Trey Mori, Daiki Gerdes, J. Christian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_13760 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Neural Network Tire Force Modeling for Automated Drifting Broadbent, Nicholas Drake Weber, Trey Mori, Daiki Gerdes, J. Christian Systems and Control Artificial Intelligence 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. |
| title | Neural Network Tire Force Modeling for Automated Drifting |
| topic | Systems and Control Artificial Intelligence |
| url | https://arxiv.org/abs/2407.13760 |