Vertical Tire Forces Estimation of Multi-Axle Trucks Based on an Adaptive Treble Extend Kalman Filter

• Main Authors: Buyang Zhang, Ting Xu, Hong Wang, Yanjun Huang, Guoying Chen
• Format: Article
• Language: English
• Published: SpringerOpen 2021-06-01
• Series: Chinese Journal of Mechanical Engineering
• Subjects: Estimation theory; Adaptive treble extend Kalman filter; Vehicle dynamics; Multi-axle truck; Vertical tire force estimation
• Online Access: https://doi.org/10.1186/s10033-021-00559-2

Abstract Vertical tire forces are essential for vehicle modelling and dynamic control. However, an evaluation of the vertical tire forces on a multi-axle truck is difficult to accomplish. The current methods require a large amount of experimental data and many sensors owing to the wide variation of the parameters and the over-constraint. To simplify the design process and reduce the demand of the sensors, this paper presents a practical approach to estimating the vertical tire forces of a multi-axle truck for dynamic control. The estimation system is based on a novel vertical force model and a proposed adaptive treble extend Kalman filter (ATEKF). To adapt to the widely varying parameters, a sliding mode update is designed to make the ATEKF adaptive, and together with the use of an initial setting update and a vertical tire force adjustment, the overall system becomes more robust. In particular, the model aims to eliminate the effects of the over-constraint and the uneven weight distribution. The results show that the ATEKF method achieves an excellent performance in a vertical force evaluation, and its performance is better than that of the treble extend Kalman filter.