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Variable stiffness magnetorheological fluid (MRF) dampers inherently have special nonlinear characteristics and complex structures. An accurate model describing the nonlinearity is the key for the damper to operate under variable conditions. This paper proposes a self-adapting model to characterize the variable stiffness MRF dampers through corresponding optimized algorithm. The experimental results verify the capability of the self-adapting of the model parameters. The model can describe the nonlinear characteristics of the variable stiffness MRF damper when conditions are changed. The proposed self-adaptive model improves the model accuracy which provide an approach for modeling complex dampers under variable working conditions.

This paper presents a magnetorheological (MR) automobile damper with a self-sensing function. The self-sensing structure is designed based on the principle of triboelectric nanogeneration. The self-sensing performance of the MR automobile damper is verified from the theoretical analysis and experimental results. A vehicle suspension vibration control system composed of 1/4 vehicle suspension, fuzzy control algorithm, and vibration excitation platform is established to test the vibration control performance of self-sensing MR automobile damper (SMRAD). The experimental results show that the fuzzy control system reduces the body acceleration of the vehicle suspension compared with the passive control. The root mean square value of vehicle suspension body acceleration is reduced by 28.8% compared with the vehicle body acceleration under passive control. This verifies the effectiveness of the self-sensing performance of the speed self-sensing structure of the MR automobile damper. The application of the SMRAD in the vehicle suspension improves the vibration reduction performance of the vehicle suspension.

Inverse models for magnetorheological (MR) devices can be used to calculate the command current required for the MR damper to generate the desired forces. Such models are of great importance in the development of semi-active vibration control. Nevertheless, it is difficult to generate inverse dynamic models because of the inherent nonlinearity of MR dampers. In this paper, we present an adaptive method for establishing the inverse model and describe the corresponding optimization algorithm. Unlike most inverse models, our self-updating model can be adjusted to describe the dynamic characteristics of MR dampers under changes in the external incentives. Experiments under different conditions verify the effectiveness of the proposed inverse model. A comparison between the proposed model and a traditional model with fixed parameters shows that the error can be reduced by approximately 20%. This demonstration of a self-updating inverse model expands the description of MR dampers and provides a new idea for vibration control under varying conditions.