Piecewise affine identification of tire longitudinal properties for autonomous driving control based on data-driven

Abstract

The tire longitudinal properties exhibit highly nonlinear dynamical behaviors influenced by the tire slip coefficient, the road longitudinal adhesion coefficient, and the tire vertical load. This paper presents a system identification approach to approximate such a nonlinear dynamic for autonomous driving controller design. The tire longitudinal properties tests are conducted using a flat-plate test bench. On the basis of the experimental data, the piecewise affine (PWA) identification of the tire longitudinal properties involves the classification of the cluster data and the parameter estimation of the affine submodels. Using the least-square algorithm, the parameter vectors of the affine submodels are estimated, and the problem of region partition is solved via heuristic approach. The simulation results of the identified PWA model match the experimental data accurately, which demonstrates the effectiveness of the proposed identification approach for the tire longitudinal properties.