تقرير
Accounts of using the Tustin-Net architecture on a rotary inverted pendulum
| العنوان: | Accounts of using the Tustin-Net architecture on a rotary inverted pendulum |
|---|---|
| المؤلفون: | van Esch, Stijn, Bonassi, Fabio, Schön, Thomas B. |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Electrical Engineering and Systems Science - Systems and Control, Computer Science - Machine Learning |
| الوصف: | In this report we investigate the use of the Tustin neural network architecture (Tustin-Net) for the identification of a physical rotary inverse pendulum. This physics-based architecture is of particular interest as it builds on the known relationship between velocities and positions. We here aim at discussing the advantages, limitations and performance of Tustin-Nets compared to first-principles grey-box models on a real physical apparatus, showing how, with a standard training procedure, the former can hardly achieve the same accuracy as the latter. To address this limitation, we present a training strategy based on transfer learning that yields Tustin-Nets that are competitive with the first-principles model, without requiring extensive knowledge of the setup as the latter. |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2408.12266 |
| رقم الأكسشن: | edsarx.2408.12266 |
| قاعدة البيانات: | arXiv |
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