Modelling and validation of magneto-rheological fluid damper behaviour under impact loading using interpolated multiple adaptive neuro-fuzzy inference system

Abstract

PurposeThe objective of this paper is to develop a fast modelling technique for predicting magneto-rheological fluid damper behaviour under impact loading applications.Design/methodology/approachThe adaptive neuro-fuzzy inference system (ANFIS) technique was adopted to predict the behaviour of a magneto-rheological fluid (MRF) damper through experimental characterisation data. In this study, an MRF damper manufactured by Lord Corporation was used for characterisation using an impact pendulum test rig. The experimental characterisation was carried out with various impact energies and constant input currents applied to the MRF damper.FindingsThis research provided a fast modelling technique with relatively less error in predicting MRF damper behaviour for the development of control strategies. Accordingly, the ANFIS model was able to predict MRF damper behaviour under impact loading and showed better performance than the modified Bouc–Wen model.Research limitations/implicationsThis study only focused on modelling technique for a single type of MRF damper used for impact loading applications. It is possible for other applications, such as cyclic loading, random loadings and system identification, to be studied in future experiments.Original/ValueFuture researchers could apply the ANFIS model as an actuator model for the development of control strategies and analyse the control performance. The model also can be replicated in other industries with minor modifications to suit different needs.