Friction Model

Friction modeling aims to accurately predict and control frictional forces in various systems, from robotic manipulators to autonomous vehicles and deep learning optimizers. Current research emphasizes developing more accurate and computationally efficient models, often employing data-driven approaches like recurrent neural networks, deep neural networks, and adaptive control algorithms, alongside physics-based models such as the LuGre model. These advancements are crucial for improving the performance and robustness of numerous applications, including precise robot control, enhanced autonomous driving capabilities, and more accurate hydrodynamic flood modeling. The development of novel sensors for measuring friction distribution further enhances the accuracy and applicability of these models.