Identification and Prediction of Magnetorheological Damper Force Based on Deep Learning

Magnetorheological dampers (MRDs) are widely used in the field of engineering vibration control. The accurate identification and prediction of their output forces are crucial for optimizing control strategies. However, traditional analysis methods based on mechanical models and empirical formulas have many limitations. This study proposes an innovative deep-learning approach. First, the continuous wavelet transform (CWT) is employed to convert the one-dimensional signals of MRD output force into two-dimensional time-frequency maps. Then, a convolutional neural network (CNN) is employed for feature extraction and type identification, and a CWT-CNN model is constructed. This model achieves 100% accuracy on the test dataset. In addition, by combining the local feature extraction ability of CNN and the sequence modeling advantage of the long short-term memory network (LSTM), a CNN-LSTM model is built to predict the MRD output force. The results show that compared with CNN and LSTM, the CNN-LSTM model exhibits stronger generalization ability. It outperforms the other models in comprehensive performance, as evaluated by MSE, RMSE, MAE, MAPE, and R2. This study provides an effective technical means for the identification and prediction of MRD output forces and promotes the application and development of deep learning in this field.