Joint vehicle state and parameters estimation via Twin-in-the-Loop observers

TL;DR

This paper introduces a Twin-in-the-Loop filtering approach for joint vehicle state and parameter estimation, leveraging a digital twin onboard to enhance robustness and reliability in varying conditions, validated with experimental data.

Contribution

It extends Twin-in-the-Loop filtering to estimate vehicle parameters, improving robustness over existing methods using experimental validation.

Findings

Significantly outperforms state-of-the-art solutions

Effective in estimating unknown vehicle parameters

Validated with real experimental data

Abstract

Vehicular control systems are required to be both extremely reliable and robust to different environmental conditions, e.g. load or tire-road friction. In this paper, we extend a new paradigm for state estimation, called Twin-in-the-Loop filtering (TiL-F), to the estimation of the unknown parameters describing the vehicle operating conditions. In such an approach, a digital-twin of the vehicle (usually already available to the car manufacturer) is employed on-board as a plant replica within a closed-loop scheme, and the observer gains are tuned purely from experimental data. The proposed approach is validated against experimental data, showing to significantly outperform the state-of-the-art solutions.