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.

A hybrid skyhook active force control for impact mitigation using magneto-rheological elastomer isolator

“…Actually, the MREID characteristic due to impact loading was developed using adaptive neuro-fuzzy inference system (ANFIS) technique by [60,61]. Since the dynamic model used for analysing the controller performance, the characteristic of MRE off-state is investigated due to the impact load.…”

“…A brief description about MREID modelling using ANFIS technique and the magnetic intensity analysis using finite element magnetic method is described in [59,61] and the result is presented in figure 9.…”

“…Based on the force results, the MREID with HySAFC controller was performed consistently in dissipating the unwanted impact force 37.49% more than the performance of MREID with skyhook controller by 21.63% compared with passive damper system, respectively. The different percentage was calculated by using the equation stated in Rahmat et al [61].…”

Enhanced Modeling of Crumple Zone in Vehicle Crash Simulation Using Modified Kamal Model Optimized with Gravitational Search Algorithm

“…It also suggests potential countermeasures to resolve the problem, such as developing a control system or using materials such as magnetorheological elastomer (MRE) to absorb the impact on the front bumper. Other related studies that have explored these solutions include Archakam and Muthuswamy [11] and Rahmat et al [12].…”

Dynamic Behaviour Modelling of Magneto-Rheological Fluid Damper Using Machine Learning

“…Recent strides highlight the proposed model's potential in elucidating MR damper dynamics (2) . Incorporating excitation amplitude and input voltage influence on damping coefficient (3) , an adaptive neuro-fuzzy inference system, driven by experimental data, adeptly forecasts MR fluid damper behavior, capturing nonlinear hysteretic traits (4,5) . This model computes damping force effectively, aligned with experimental outcomes within a preset frequency and coil current span (6) .…”