An Accurate and Generic Testing Approach to Vehicle Stability Parameters Based on GPS and INS

Sensors

Zhibin Miao¹  Hongtian Zhang¹  Jinzhu Zhang² 

[1] College of Power and Energy Engineering, Harbin Engineering University, Harbin 150001, China;Heilongjiang Institute of Technology, Harbin 150050, China;

关键词: data fusion;    Kalman filter;    GPS/INS;    fuzzy logical system;    vehicle stability parameters;   

DOI  :  10.3390/s151229812

来源: mdpi

【 摘 要 】

With the development of the vehicle industry, controlling stability has become more and more important. Techniques of evaluating vehicle stability are in high demand. As a common method, usually GPS sensors and INS sensors are applied to measure vehicle stability parameters by fusing data from the two system sensors. Although prior model parameters should be recognized in a Kalman filter, it is usually used to fuse data from multi-sensors. In this paper, a robust, intelligent and precise method to the measurement of vehicle stability is proposed. First, a fuzzy interpolation method is proposed, along with a four-wheel vehicle dynamic model. Second, a two-stage Kalman filter, which fuses the data from GPS and INS, is established. Next, this approach is applied to a case study vehicle to measure yaw rate and sideslip angle. The results show the advantages of the approach. Finally, a simulation and real experiment is made to verify the advantages of this approach. The experimental results showed the merits of this method for measuring vehicle stability, and the approach can meet the design requirements of a vehicle stability controller.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.