LAUSR

Abstract Magneto-rheological dampers are an effective technology to control the damping coefficient of a semi-active suspension. Most of the contributions in literature propose damper models to be used in simulation, or as damping force virtual sensors in control applications. Typically, phenomenological models or complex black-box approaches, relying on Neural Networks, are employed. In this work, we propose a semi-active MR model based on a Hammerstein–Wiener scheme, meant not only for force estimation but also – in a more genuinely control-oriented perspective – to be proactively used in the suspension controller design. Despite being a black-box model, each component is shown to serve for the characterization of a specific feature of the MR damper, and its identification is done thanks to an ad hoc design of experiments. In particular, the Wiener part of the model is shown to be essential for the proper modelling of the magnetization dynamics of the magneto-rheological fluid, which usually is a neglected aspect in control-oriented models. The proposed scheme is validated on a testbench using realistic road solicitations.