Neural-Network Model of a Magneto-Rheological Damper

Abstract

The topic of this paper is the identification of an accurate model for Magneto-Rheological (MR) dampers. A semi-active MR-damper is a dynamic system, where the inputs are the stroke and the command current; the current is the control input which modulates at high-bandwidth the damping characteristic through the variation of a magnetic field. The output is the force delivered by the damper. Among the broad set of applications where MR-dampers can be used, the results proposed in this work refer to a MR-dampers for the control of vehicle dynamics. MR-damper are highly-nonlinear systems, and their accurate modeling is a non-trivial task. MR-dampers can be modeled using two different model classes: semi-physical models and black-box models. Both approaches are considered in this work.