2024 Volume 19 Issue 5 Pages 454-463
In various dynamic systems, accurately predicting frictional stick–slip motion is critical for maintaining stability. To address this issue, we integrated a particle filter with a rate- and state-dependent friction model to enhance the prediction of stick–slip dynamics. Our method assimilates the experimental data and iteratively updates the parameter distributions to align with the observed stick–slip events. Using the proposed approach, we demonstrate parameter estimation for different dynamic behaviors of a one-degree-of-freedom system, enabling a robust simulation of stick–slip motion. Specifically, as the particle filter assimilates data, the prediction interval for the candidate parameter set becomes narrow, thereby yielding increasingly precise forecasts. Subsequently, by utilizing the posterior parameter distributions obtained through Bayesian updates, our approach successfully simulated and predicted the experimental stick–slip outcomes. This validation underlines the capability of the rate- and state-dependent friction model to describe complex frictional motions, and suggests the potential of the particle filter as a powerful Bayesian tool for predicting nonlinear dynamics, including stick–slip motion.