Parameter Identification of LuGre Tire Model for the Simplified Motion Dynamics of a Quarter-vehicle Model Based on Ant Colony Algorithm

In light of the high nonlinearity of LuGre friction model, a novel method based on ant colony algorithm (ACA) for identifying the friction parameters of LuGre tire model is proposed. ACA is a parallelized bionic optimization algorithm inspired from the behavior of real ants, and a kind of positive feedback mechanism is adopted in ACA. On the basis of brief introduction of LuGre friction model, a method for identifying the static LuGre friction parameters and the dynamic LuGre friction parameters using ACA is derived. Finally, this new friction parameter identification scheme is applied to the simplified motion dynamics of a quarter-vehicle model with high precision. Simulation and application results verify the feasibility and the effectiveness of the scheme. It provides a new way to identify the friction parameters of LuGre tire model.