Miao Zhibin, Zhang Hongtian, Zhang Jinzhu
College of Power and Energy Engineering, Harbin Engineering University, Harbin 150001, China.
Heilongjiang Institute of Technology, Harbin 150050, China.
Sensors (Basel). 2015 Dec 4;15(12):30469-86. doi: 10.3390/s151229812.
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.
随着汽车工业的发展，车辆稳定性控制变得越来越重要。对车辆稳定性评估技术的需求很高。作为一种常用方法，通常应用GPS传感器和INS传感器，通过融合来自这两个系统传感器的数据来测量车辆稳定性参数。虽然在卡尔曼滤波器中需要识别先验模型参数，但它通常用于融合多传感器数据。本文提出了一种用于测量车辆稳定性的鲁棒、智能且精确的方法。首先，提出了一种模糊插值方法以及四轮车辆动力学模型。其次，建立了融合GPS和INS数据的两阶段卡尔曼滤波器。接下来，将该方法应用于一个案例研究车辆，以测量横摆率和侧偏角。结果显示了该方法的优势。最后，进行了仿真和实际实验，以验证该方法的优势。实验结果表明了该方法在测量车辆稳定性方面的优点，并且该方法能够满足车辆稳定性控制器的设计要求。
