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

Chinese Journal of Mechanical Engineering

Yanjun Huang¹  Ting Xu²  Buyang Zhang³  Guoying Chen⁴  Hong Wang⁵ 

[1] Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, Canada;Jihua Laboratory, 528200, Foshan, Guangdong, China;Jihua Laboratory, 528200, Foshan, Guangdong, China;State Key Laboratory of Automotive Simulation and Control, Jilin University, 130022, Changchun, China;State Key Laboratory of Automotive Simulation and Control, Jilin University, 130022, Changchun, China;Tsinghua Intelligent Vehicle Design and Safety Research Institute, Tsinghua University, 100084, Beijing, China;

关键词: Estimation theory;    Adaptive treble extend Kalman filter;    Vehicle dynamics;    Multi-axle truck;    Vertical tire force estimation;   

DOI  :  10.1186/s10033-021-00559-2

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY