摘要
为了解决六维力传感器输出信号不可避免地会被随机噪声干扰而造成准确性不佳的问题,从力传感器的简化模型出发,设计了一种基于自适应卡尔曼滤波和滑动平均值滤波的混合滤波算法。采用改进的卡尔曼滤波算法,引入渐消因子来加大当前观测量的权重,减小初始偏差带来的影响,同时将新息方差引入卡尔曼增益中来动态调整预测模型,降低过程噪声和测量噪声的干扰。最后进行滑动平均值滤波处理,得到最终的滤波数据。实验比较结果表明,混合滤波算法性能更好,有效地滤除了干扰信号,提高了传感器输出信号的稳定性和平滑性。
In order to solve the problem that the output signal of the six-axis force sensor is inevitably disturbed by random noise,resulting in poor accuracy.Thus,this paper designs a hybrid filtering algorithm based on adaptive Kalman filtering and sliding mean filtering from the simplified model of force sensor.The improved Kalman filtering algorithm is used to introduce the fading factor to increase the weight of the current observation and reduce the impact of the initial bias,while the residual variance is drawn into the Kalman gain to dynamically adjust the prediction model to reduce the system noise and measurement noise.Then a sliding average filtering process is performed to obtain the final filtered data.The experimental results show that as against common Kalman filter,the hybrid filter effectively eliminates random interference signals and improves the stability and smoothness of output signal.
作者
张文祥
徐林森
孔令成
ZHANG Wenxiang;XU Linsen;KONG Lingcheng(School of Microelectronics and Control Engineering,Changzhou University,Changzhou 213164,China;College of Mechanical and Electrical Engineering,Hohai University,Changzhou 213022,China)
出处
《组合机床与自动化加工技术》
北大核心
2023年第11期47-50,55,共5页
Modular Machine Tool & Automatic Manufacturing Technique
基金
江苏省前沿引领技术基础研究专项项目(BK20192004)
苏州市重点产业技术创新-前瞻性应用研究项目(SYG202143)。
