基于卡尔曼滤波的自平衡两轮电动车多传感器信息融合研究  

The Multi-Sensor Information Fusion Research of SelfBalancing Two-Wheeled Electric Vehicle Based on EKF

作　　者：朱军[1] 刘慧君[1] 李香君[1] 王栋[1] 

机构地区：[1]河南理工大学电气工程与自动化学院,焦作454000

出　　处：《电气工程学报》2016年第6期25-32,共8页Journal of Electrical Engineering

基　　金：河南省高校基本科研业务费专项资金资助项目(NSFRF140115);河南省教育厅科学技术重点研究项目(12A4700);河南理工大学博士基金资助项目(B2011-104);中国煤炭工业协会科学技术研究项目(MTKJJ2012-376)

摘　　要：针对MEMS惯性传感器在两轮自平衡车姿态检测中存在随机漂移误差的问题,利用扩展卡尔曼滤波实现对加速度计与陀螺仪的信息融合,设计实用的滤波算法,根据实验获得的惯性传感器误差特性,采用Levenberg-Marquardt非线性最小二乘迭代法拟合数据,建立自平衡车导航用惯性传感器陀螺仪和加速度计误差的数学模型,对加速度传感器的随机误差和陀螺仪的温度漂移误差进行补偿,从而得到自平衡车姿态信号的最优估计,实现两轮自平衡车的自平衡运行。实验结果分析表明,采用卡尔曼信息融合方法,得到自平衡车姿态信息最优估计是有效可行的,并且有利于两轮车完成自平衡控制。To solve the random drift error problem for MEMS inertial sensor in selfbalancing two-wheeled electric vehicle gesture measuring,the EKF practical filtering algorithm is used to realize the information fusion of accelerometer and gyroscope.According to the experiment result of inertial sensor error characteristics,using Levenberg-Marquardt nonlinear least-squares iteration fitting data,and establishing the mathematical error models to compensate the random error of the acceleration sensor and the temperature drift error of the gyroscope,then the posture signal of self-balancing electric vehicle can be estimated optimally.The experimental based on REKF information fusion result shows that the self-balancing electric vehicle posture signal optimal estimation is effective and feasible,and it is beneficial to the two-wheeled electric vehicle self-balancing control.

关 键 词：两轮自平衡车 卡尔曼滤波算法 加速度值 角速度值 陀螺仪 

分 类 号：TM315[电气工程—电机]