Road condition monitoring system using the parameters identification of LuGre tyre friction model

In this talk we are concerned with a new development of an on-line road condition monitoring methodology and its implementation using PDE form of LuGre tyre-road friction model. The road condition is recursively evaluated regarding slipperiness and is classified into three grades, normal (_ _ 1), i% slippery (1 < _ < 5), and mixed friction surface (_ is varying). The linearized tyre model parameters are estimated and then converted to the initial values of identification algorithm through the multivariate interpolation. An iterative parameter identification algorithm that utilizes steady state solution of the PDE form of LuGre, is used to identify the road condition parameters. In order to have a fast, robust and efficient identification algorithm, a minimizing quadratic function subject to an ellipsoidal constraint is considered, which is guaranteed to yield an optimal solution in a finite number of iterations. The talk is complemented by a performance report of the new algorithm for various road conditions. Our numerical testings are very promising and show the practical viability of the algorithm.