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

arXiv 2401.10945 ↗First posted 18 Jan 2024eess.SY cs.LG cs.SYcited by 0

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Abstract (arXiv)

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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References · 15

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  12. 2022The Twin-in-the-Loop approach for vehicle dynamics controlcited by 2
3 more
  1. 2023Joint vehicle state and parameters estimation via Twin-in-the-Loop observerscited by 2
  2. 2022Twin-in-the-loop state estimation for vehicle dynamics control: theory and experimentscited by 1
  3. 2023Optimization tools for Twin-in-the-Loop vehicle control design: analysis and yaw-rate tracking case studycited by 1

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