Automatic dimensionality reduction of Twin-in-the-Loop Observers
This work addresses complexity reduction in vehicle dynamics estimation for automotive applications, but it is incremental as it builds on an existing observer architecture.
The paper tackled the high-dimensional optimization problem in Twin-in-the-Loop Observers for vehicle dynamics estimation by developing a procedure to reduce observer complexity using supervised and unsupervised learning approaches, validated with real-world data for longitudinal and lateral dynamics.
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.