Tyre-road adherence conditions estimation for intelligent vehicle safety applications

Abstract

It is well recognized in the automotive research community that knowledge of the real-time tyre-road friction conditions can be extremely valuable for intelligent safety applications, including design of braking, traction, and stability control systems. This paper presents a new development of an on-line tyre-road adherence estimation methodology and its implementation using both Burckhardt and LuGre tyre-road friction models. The proposed strategy first employs the recursive least squares to identify the linear parameterization (LP) form of Burckhardt model. The identified parameters provide through a Takagi-Sugeno (T-S) fuzzy system the initial values for the LuGre model. Then, it is presented a new large-scale optimization based estimation algorithm using the steady state solution of the partial differential equation (PDE) formof LuGre to obtain its parameters. Finally, real-time simulations in various conditions are provided to demonstrate the efficacy of the algorithm.