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

• URL: http://arxiv.org/abs/2401.10945v1
• Date: Thu, 18 Jan 2024 10:14:21 GMT
• Title: Automatic dimensionality reduction of Twin-in-the-Loop Observers
• Authors: Giacomo Delcaro, Federico Dett\`u, Simone Formentin, Sergio Matteo Savaresi
• Abstract summary: This paper aims to find a procedure to tune the high-complexity observer by lowering its dimensionality. The strategies have been validated for speed and yaw-rate estimation on real-world data.
• Score: 1.6877390079162282
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: State-of-the-art vehicle dynamics estimation techniques usually share one common drawback: each variable to estimate is computed with an independent, simplified filtering module. These modules run in parallel and need to be calibrated separately. To solve this issue, a unified Twin-in-the-Loop (TiL) Observer architecture has recently been proposed: the classical simplified control-oriented vehicle model in the estimators is replaced by a full-fledged vehicle simulator, or digital twin (DT). The states of the DT are corrected in real time with a linear time invariant output error law. Since the simulator is a black-box, no explicit analytical formulation is available, hence classical filter tuning techniques cannot be used. Due to this reason, Bayesian Optimization will be used to solve a data-driven optimization problem to tune the filter. Due to the complexity of the DT, the optimization problem is high-dimensional. This paper aims to find a procedure to tune the high-complexity observer by lowering its dimensionality. In particular, in this work we will analyze both a supervised and an unsupervised learning approach. The strategies have been validated for speed and yaw-rate estimation on real-world data.

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