Automatic dimensionality reduction of Twin-in-the-Loop Observers
- URL: http://arxiv.org/abs/2401.10945v2
- Date: Mon, 21 Jul 2025 17:13:38 GMT
- Title: Automatic dimensionality reduction of Twin-in-the-Loop Observers
- Abstract summary: This paper focuses on developing a procedure to reduce the observer's complexity by exploring both supervised and unsupervised learning approaches.<n>The effectiveness of these strategies is validated for longitudinal and lateral vehicle dynamics using real-world data.
- Score: 1.440041584757981
- License: http://creativecommons.org/licenses/by/4.0/
- 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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