Application and Comparison of a Friction Force Model by Using Physic Informed Neural Network

This research discovers the ability on using Physics-Informed Neural Network (PINN) for modelling friction force. The training data is generated using the Runge-Kutta method, and the physical laws incorporated into the PINN are based on the LuGre model. To evaluate the effectiveness of this model, this research discusses both forward and inverse method. The results show that, whether using the forward or inverse method, the prediction time is minimized while maintaining a high level of accuracy. Although the physics-based loss function proves valuable, it is crucial not to overly depend on it. Additionally, the number of data points and iterations wield significantly influence on the outcomes. PINN is effective for constructing precise friction force models, but it is necessary to repeat the experiments to ensure the best performance of this method.