Nonlinear observer of sideslip angle using a particle filter estimation methodology

Abstract The knowledge of the vehicle dynamic is important to improve the stability control of modern automotive engineering. Some key variables of the vehicle dynamic, such as the sideslip angle, are difficult to measure directly for technology or economic reasons. Lots of algorithms have been proposed to estimate these variables. To the best of our knowledge, most of these algorithms are based on linearization techniques, among which the extended Kalman filter is the most frequently used algorithm in the unmeasurable variable estimation for automotive control. In this paper, we propose two new nonlinear observers which uses the particle filter and the modified bootstrap filter to estimate the sideslip angle, respectively. These observers are based on the nonlinear double track model, in which the Dugoff model is used to describe the relation between the tire road forces and the sideslip angle. The good performances of these two observers are demonstrated by two classic experiments.

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