Brake-by-Wire (BBW) actuators are the current frontier of the Ride-by-Wire paradigm applied to modern and future vehicles. The control of such actuators has reached a certain scientific maturity, but the calibration of the proposed solutions is strongly dependent on the availability of a system model. In the pursuit of a faster, automatic and possibly continuous - during the component lifetime - calibration of the control logic, data-driven model-free tuning techniques represent an interesting opportunity. In this paper, this topic is addressed by using the Virtual Reference Feedback Tuning (VRFT) approach, applied on the BBW cascade control architecture in [1]. The data-driven tuning shows equivalent closed-loop performances with respect to the standard model-based approach, adding two benefits: the simpler (thus faster) experimental campaign to collect the necessary data and the possibility of using normal system operation data for the tuning, paving the way for the automatic maintenance of such systems, which suffers the effect of ageing and wearing.

A data-driven approach for fast controller calibration of Brake-by-Wire actuators

Radrizzani S.;Todeschini D.;Riva G.;Formentin S.;Panzani G.;Savaresi S. M.
2020-01-01

Abstract

Brake-by-Wire (BBW) actuators are the current frontier of the Ride-by-Wire paradigm applied to modern and future vehicles. The control of such actuators has reached a certain scientific maturity, but the calibration of the proposed solutions is strongly dependent on the availability of a system model. In the pursuit of a faster, automatic and possibly continuous - during the component lifetime - calibration of the control logic, data-driven model-free tuning techniques represent an interesting opportunity. In this paper, this topic is addressed by using the Virtual Reference Feedback Tuning (VRFT) approach, applied on the BBW cascade control architecture in [1]. The data-driven tuning shows equivalent closed-loop performances with respect to the standard model-based approach, adding two benefits: the simpler (thus faster) experimental campaign to collect the necessary data and the possibility of using normal system operation data for the tuning, paving the way for the automatic maintenance of such systems, which suffers the effect of ageing and wearing.
2020
CCTA 2020 - 4th IEEE Conference on Control Technology and Applications
978-1-7281-7140-1
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1151719
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