基于模型辨识的SMC滤波技术应用研究  

Application study on SMC filtering techniques based on model identification

作　　者：张丽妹[1] 高占宝[1] 尹志兵 

机构地区：[1]北京航空航天大学自动化科学与电气工程学院,北京100191 [2]中山市精威包装机械有限公司,中山528425

出　　处：《仪器仪表学报》2013年第10期2287-2292,共6页Chinese Journal of Scientific Instrument

基　　金：北京市自然科学基金(4113073);中央高校基本科研业务费专项资金(YWF-10-02-096)资助项目

摘　　要：针对组合秤大小料斗开关门扰动与物理参数不准确的实际问题,提出了基于高斯和粒子滤波器的序贯蒙特卡罗(SMC)动态称重数据处理新方法。通过对称重信号的频谱分析,指出了物理建模方法的不足之处,采用负阶跃动态校准实验数据辨识得到对象模型,并利用伽马分布的非对称拖尾特性对大小料斗开关门扰动等低频噪声进行建模得到噪声模型;在此模型的基础上,针对系统非高斯噪声特性,选择了基于高斯和粒子滤波器的SMC方法对信号进行滤波处理。实验及仿真结果表明,高斯和粒子滤波可以有效地滤除开关门扰动,有效地提高动态称重的速度与精度,优于传统的卡尔曼滤波和粒子滤波。Aiming at the actual problems of the combination weigher hopper door switching disturbances and inac, cu- rate physical parameters,a new sequential Monte Carlo （SMC） data processing method for dynamic weighing hased on the Gaussian sum particle filter is presented. Through analyzing the spectrum of the weighing signal, the flaw of the physical modeling method is pointed out. The negative-step data of the dynamic calibration experiment are used to identify and obtain the object model ; the asymmetric trailing characteristic with gamma distribution is adopted to mod- el the low-frequency noise of the hopper door switching disturbances, and the noise model is obtained. On the basis of the identification model with gamma noise characteristic,a new SMC method based on the Gaussian sum particle filter is selected to process the dynamic weighing data. The simulation and experiment study results show that the Gaussian sum particle filter can effectively filter the door switching disturbances, so as to significantly improve the accuracy and speed of the dynamic weighing,and is better than traditional KF and PF

关 键 词：动态称重 序贯蒙特卡罗 高斯和粒子滤波 模型辨识 

分 类 号：TN911.72[电子电信—通信与信息系统]