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Accurate control of the magnetorheological damper (MRD) damping force and current is necessary to realize the effective semi-active suspension control. However, the temperature sensitivity of the magnetorheological fluid makes the MRD force strongly dependent on temperature changes, leading to the problem of the model mismatch and degradation of control effect. In this paper, the experimental study of MRD at different currents and velocities from −40 °C to 80 °C was implemented. It reveals the characteristic of MRD damping loss at low temperatures and viscous damping reduction at high temperatures. On this basis, a new parametrized hyperbolic hysteresis model with temperature as an independent variable is proposed, providing an accurate description of the viscosity, stiffness, and hysteresis characteristics of the MRD. A simplified temperature-revised inverse model is proposed to calculate the driving current with demanding force. It could improve the accuracy of driving current by 12.79% and demanding force by 18.67%. A process in the loop simulation is implemented to validate the inverse model with a modified non-chattering algorithm. Together with the inverse model, the proposed algorithm could realize continuous current change, reducing the RMS of acceleration by 14% on road of class B. Furthermore, the temperature compensation could improve the control effect by 19.78%.

Magnetorheological (MR) damper is one of the key technologies in the field of semi-active suspensions. However, the dynamic model and control strategy are not yet well established. A unified MR damper model and its inverse characterization are proposed to investigate MR damper dynamics. First, a novel unified model is proposed to describe the shear term of the MR damper force. This forward unified MR damper model uses one set of parameters to describe all MR force behavior under all currents and velocities, which agrees well with experimental data. Then, an inverse MR model based on the adaptive neuro-fuzzy inference system (ANFIS) technique has been established, where the precise training data are obtained from the proposed unified hysteretic model. A clear step-wise modeling process is proposed to decrease coupling degree of system which can be beneficial to improve inverse modeling efficiency. The inverse MR model is used for a semi-active suspension control, which can obtain the excitation current, exactly tracking the desired damping force computed by suspension control algorithm. Finally, validation of the proposed ANFIS inverse model has been conducted by comparison with three traditional empirical inverse approaches. The results reveal that the ANFIS inverse model could achieve much more tracking capability for the desired damping force and thus contributes to the development of semi-active suspension controllers.

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