Reliability analysis of vehicle semi-active suspension systems under parameter uncertainties in magnetorheological dampers

Magnetorheological dampers (MR) have become essential components in recent years for vibration control in dynamic systems. Their effective application relies on the calibration of models using experimental data. However, these models are often assumed to have deterministic parameters, which can negatively impact the reliability of the control system by neglecting uncertainties. In this work, we propose a methodology to assess the reliability of a vehicle semi-active suspension system under parameter uncertainties in an MR damper model. First, we calibrate the Kwok's MR damper model using Bayesian methods across a wide range of input currents, excitation frequencies, and displacements. Then, we design a procedure to identify the strongest nonlinear response of the semi-active suspension system, enabling the construction of surrogate models for uncertainty propagation without excessive computational cost. Finally, we apply this approach to estimate the probability density functions (PDFs) of the MR damper model parameters for untested input currents. This study demonstrates the application of existing uncertainty quantification techniques to improve the identification of parameter uncertainties in smart semi-active suspension systems. • A complete framework for uncertainty analysis of semi-active suspensions is presented. • A procedure to identify the strongest non-linear behaviour in uncertain dynamic systems. • A new method to find MR damper parameters uncertainties for non-tested current inputs. • The risk of dynamic system instability due to MR damper model uncertainty is reduced. • The method can be applied to other non-linear systems without burden computation.