Abstract
This paper describes a vehicle state variable estimator for an electronic stability program control system that operates using an extended Kalman filtering (EKF) algorithm. The proposed estimator uses the steering angle and vertical forces on the tires as inputs. Subsequently, according to a seven-degree-of-freedom vehicle model and the measured sensor signals regarding longitudinal and lateral accelerations, the steering angle, the yaw rate, and the wheel speed, the EKF algorithm is used to identify unmeasurable state variables such as lateral velocity, the vehicle side-slip angle, and lateral tire forces. The estimation results of the proposed control system exhibited high performance.
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Chen, CK., Le, AT. (2016). Vehicle Side-Slip Angle and Lateral Force Estimator Based on Extended Kalman Filtering Algorithm. In: Duy, V., Dao, T., Zelinka, I., Choi, HS., Chadli, M. (eds) AETA 2015: Recent Advances in Electrical Engineering and Related Sciences. Lecture Notes in Electrical Engineering, vol 371. Springer, Cham. https://doi.org/10.1007/978-3-319-27247-4_33
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DOI: https://doi.org/10.1007/978-3-319-27247-4_33
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