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In vehicle dynamics control, many variables of interest cannot be directlymeasured, as sensors might be costly, fragile, or even not available.Therefore, real-time estimation techniques need to be used. The previousapproach suffers from two main drawbacks: (i) the approximations due to modelmismatch might jeopardize the performance of the final estimation-basedcontrol; (ii) each new estimator requires the calibration from scratch of adedicated model. In this paper, we propose a twin-in-the-loop scheme, where thead-hoc model is replaced by an accurate multibody simulator of the vehicle,typically available to vehicles manufacturers and suitable for the estimationof any onboard variable, coupled with a compensator within a closed-loopobserver scheme. Given the black-box nature of the digital twin, a data-drivenmethodology for observer tuning is developed, based on Bayesian optimization.The effectiveness of the proposed estimation method for the estimation ofvehicle states and forces, as compared to traditional model-based Kalmanfiltering, is experimentally shown on a dataset collected with a sports car.

Twin-in-the-loop state estimation for vehicle dynamics control: theory and experiments