In an era marked by the demand for unprecedented levels of precision in engineering applications, the profound impact of friction forces on motion control systems cannot be underestimated. This thesis extensively investigates the frictional behavior of the Proton Motion Stage, an advanced high-precision motion control system developed by Prodrive Technologies. This research conducts both experimental investigations and computational simulations, offering valuable insights into its friction behavior across diverse conditions and scenarios.
The research begins with an analysis of existing models used to describe friction behavior in precision engineering systems. A critical evaluation of empirical models highlighting strengths and limitations is presented, and the LuGre friction model is selected. Subsequently, a simulation work is conducted to identify the viscous coefficients, the stiffness coefficient, the Coulomb friction, the Stribeck friction, and the Stribeck velocity in the LuGre model. The simulation setup is described, including the incorporation of the LuGre friction model and the identification of system parameters. The accuracy of the identification value to the true value is above 99\%. A comparison of the sensitivity of the objective function to the change of parameters is also conducted to enable a comprehensive exploration of friction dynamics. Finally, the research delves into static and dynamic parameter experiments, where cable slab forces' position-dependent impacts and velocity-friction maps that capture the intricate Stribeck effect are presented, and closed-loop and open-loop setups to dissect friction behavior during rapid motion changes are employed. Residual analysis of histogram and 90\% confidence autocorrelation and cross-correlation is also presented to study the quality of identification and shows that the LuGre model does not fully capture the friction phenomena on the Proton Motion Stage. Future research should involve the modification of the LuGre model and data-driven approaches such as machine learning. Overall, this thesis fills the gap in state-of-the-art works by combining theory and practice to enhance the understanding of friction in precision engineering systems.