In this paper, artificial neural network (ANN) based models to capture the dynamic engagement torque of a wet clutch system are developed and analyzed. A two-layer recurrent ANN with output feedback is chosen as the baseline architecture since its simplicity allows easy implementation and efficient execution. Although this model exhibits good performance in capturing the overall mean level of the engagement torque as a function of time, it is unable to predict some of the important clutch dynamics behaviors. To improve the performance, additional neurons that represent the first principles of the clutch engagement process are incorporated into the neural network model. In other words, a hybrid model in which physical knowledge is implicitly embedded within the ANN architecture is derived. This hybrid model is trained and tested with experimental data. The results show that the performance of the hybrid network is much superior to that of the baseline ANN. It can successfully capture not only the trends, but also the detailed characteristics of the clutch engagement torque as a function of time.

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