Modelling of a magneto-rheological fluid dual clutch with BP neural network

Publication Name

International Journal of Powertrains


In this paper, a backpropagation (BP) neural network model for a novel magneto-rheological fluid dual-clutch (MRFDC) is presented. The MRFDC is a complicated system with high nonlinearity and strong hysteresis, and the conventional parametric modelling methods are based on parameter identification and optimisation. Thus, the modelling work is usually difficult, and the performance of conventional models is usually not good enough for the MRFDC. In contrast, the proposed BP neural network model in this work is easily obtained and able to precisely describe the input and output relationship of the MRFDC. To be specific, the proposed BP neural network model approximates the dynamic behaviours of the MRFDC regarding dynamic input currents and rate-dependent hysteresis. The model input variables are selected considering the working mode of the MRFDC and its rate-dependent dynamic magnetic hysteresis. Then, the BP neural network is trained by the input and output datasets obtained from experiments. The model performance is validated by experiments, and experimental results show that the proposed model is able to predict the output torque capacity of the MRFDC precisely with dynamic input currents.

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Funding Sponsor

Australian Research Council



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