Application of Extended Kalman Filtering for Estimating Immeasurable Vehicle State Variables

Abstract

This paper presents an extended Kalman filtering (EKF) algorithm for estimating immeasurable state variables of a vehicle stability control system. Initially, the steering angle and vertical forces on the tires were considered inputs of the estimator. The measured process outputs were the sensor signals egarding longitudinal and lateral accelerations, steering angle, yaw rate, and wheel speed. Subsequently, by using Euler discretization for a seven-degree-of-freedom nonlinear vehicle model, difficult-to-measure state variables such as lateral velocity, vehicle side-slip angle, and lateral tire forces were identified separately by using the EKF algorithm. The estimation results of the proposed control system evidenced high performance.

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