Abstract
Nonlinear friction could affect the high-precision motion system, resulting in poor tracking accuracy in the end. This is due to the fact that the Lugre friction model's parameter identification process comprises both static and dynamic parameter identification. The convolutional neural network (CNN) model is used in this study to create the friction identification system. We suggest a hybrid methodology that combines the CNN method and the classic least-squares technique. The convolutional layer (CONV), which is defined by a convolutional kernel, analyzes and extracts features from an input image. In terms of accuracy and convergence, the results reveal that the upgraded CNN friction model outperforms the original CNN friction model. You may successfully reduce the influence of friction on your system while improving its performance by applying the feedforward correction.