Accounts of using the Tustin-Net architecture on a rotary inverted pendulum
<|reference_start|>Accounts of using the Tustin-Net architecture on a rotary inverted pendulum: 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.<|reference_end|>
@article{van esch2024accounts,
    title={Accounts of using the Tustin-Net architecture on a rotary inverted
  pendulum},
    author={Stijn van Esch, Fabio Bonassi, Thomas B. Sch"on},
    journal={arXiv preprint arXiv:2408.12266},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.12266},
    primaryClass={eess.SY cs.LG cs.SY}
}