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.
@article{arxiv.2401.10945,
title = {Automatic dimensionality reduction of Twin-in-the-Loop Observers},
author = {Giacomo Delcaro and Riccardo Poli and Federico Dettù and Simone Formentin and Sergio Matteo Savaresi},
journal= {arXiv preprint arXiv:2401.10945},
year = {2025}
}