摘要
为了减少温度对MEMS陀螺测量精度的影响,提出了一种采用阵列技术的MEMS陀螺仪温度误差补偿新方法。该方法不需要进行温度测量,通过对多陀螺输出进行数据融合以消除温度对陀螺仪输出的影响,达到正确检测角速率的目的,简化了系统结构。采用遗传算法优化的BP神经网络来进行陀螺阵列数据的融合,抛开通常把零偏和标度因数分开建模补偿的思想,将两者统一进行补偿。实验结果表明,该方法能够大幅度降低温度造成的陀螺误差,从而满足工程应用需要。
In order to decrease temperature related errors in MEMS gyroscope,a novel method based on array technique is proposed for temperature compensation of the gyroscope. The advantage of this method is that it is not need to measure the temperature,in stead,data fusion of the output of several gyroscopes is used to reduce the influence of temperature,and the system structure is simplified. The BP neural network optimized by genetic algorithms is used in data fusion of the gyroscope data,which compensates the error of the output of gyroscopes directly,while the zero bias and scale factors are compensated separately in general. The results of the experiments show that the method proposed can significantly reduce the error of gyroscope caused by temperature,and thus can improve the accuracy.
出处
《电光与控制》
北大核心
2015年第7期93-97,共5页
Electronics Optics & Control
