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arXiv · 2401.10945

Automatic dimensionality reduction of Twin-in-the-Loop Observers

Giacomo Delcaro·Riccardo Poli·Federico Dettù·Simone Formentin·Sergio Matteo Savaresi
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Abstract

Conventional vehicle dynamics estimation methods suffer from the drawback of employing independent, separately calibrated filtering modules for each variable. To address this limitation, a recent proposal introduces a unified Twin-in-the-Loop (TiL) Observer architecture. This architecture replaces the simplified control-oriented vehicle model with a full-fledged vehicle simulator (digital twin), and employs a real-time correction mechanism using a linear time-invariant output error law. Bayesian Optimization is utilized to tune the observer due to the simulator's black-box nature, leading to a high-dimensional optimization problem. This paper focuses on developing a procedure to reduce the observer's complexity by exploring both supervised and unsupervised learning approaches. The effectiveness of these strategies is validated for longitudinal and lateral vehicle dynamics using real-world data.

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Giacomo Delcaro, Riccardo Poli, Federico Dettù, Simone Formentin, Sergio Matteo Savaresi. 2024-01-18. Automatic dimensionality reduction of Twin-in-the-Loop Observers. https://arxiv.org/abs/2401.10945

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