基于信号融合的自平衡车姿态测量方法研究

Research on Signal-fusion Based Attitude Angle Measurement of Self-balanced Car

针对自平衡车姿态角度测量问题,本文叙述了基于卡尔曼滤波和互补滤波的自平衡车MEMS IMU单轴融合算法的原理,分析了平衡车车身翻滚运动对融合算法计算结果的影响,最后分析了卡尔曼滤波法和互补滤波法的动态和静态收敛速度。为了验证各算法的效果,本文搭建了基于飞思卡尔K60单片机的信号采集平台进行实验。实验表明在采用低成本MEMS IMU的自平衡车俯仰角计算中,两种融合算法的效果接近,但是互补滤波法在静态时的收敛速度较快,同时考虑计算时效性,采用基于互补滤波的单轴融合算法较为合适。

The principle of algorithms based on Kalman filtering and complementary filtering are presented to measure the attitude angle of a self-balance car.The impact of rolling motion of car is evaluated.The speed of convergence of Kalman filtering and complementary filtering is analyzed.A K60 microcontroller based experimental platform is developed to validate the efficiency of algorithms.Result of experiment shows that the performance of the two signal-fusion algorithms are approaching.But after considering the computational efficiency,we can conclude that the complementary filtering based algorithm is more suitable.