Joint unscented Kalman filter for state and parameter estimation in vehicle dynamics

Abstract

Advanced driver assistance systems in modern vehicles have gained interest in the past decades. For most of these systems accurate knowledge about the current driving state, describing the vehicle's stability, and certain parameters is beneficial for improved performance. Especially, a robust estimation of the vehicle's side-slip angle, and, furthermore, knowledge about some influential system parameters, like the vehicle's mass or its moment of inertia, has vast potential to improve the state estimation's accuracy and, therefore, improve the assistance system's performance. In this paper an online estimation of the vehicle's side-slip angle and additional estimation of the mass and moment of inertia, separately and simultaneously is presented using the joint Unscented Kalman Filter. The state estimation results are validated by comparing to measurements taken on a VW Golf VII. The parameter estimation results are verified by comparing to results obtained using a global offline identification algorithm.