基于卡尔曼滤波观察器的优化预测控制方法研究  

Research on Optimal Predictive Control Method Based on Kalman Filter Observer

作　　者：陈霈[1] 冯康康[1] 管晓晨 石祥建[1] 杨玉 

机构地区：[1]南京南瑞继保电气有限公司,江苏南京211102

出　　处：《工业控制计算机》2023年第10期43-46,共4页Industrial Control Computer

摘　　要：为解决预测控制中由于模型失配所导致的控制效果变差甚至系统振荡发散的问题,提供了一种基于卡尔曼滤波观察器的优化预测控制方法,在广义预测控制基础上,增加了基于卡尔曼滤波的状态观察器,以卡尔曼滤波所观察的数据替代实测控制量进行广义预测控制偏差的计算,并且引入卡尔曼滤波模型精度误差的收缩因子,防止卡尔曼滤波观察器陷入静态误差陷阱,从而有效抑制当模型失配时导致的波动甚至振荡,并消除静态偏差。In order to solve the problem of poor control effect and even system oscillation divergence caused by model mismatch in predictive control,this paper provides an optimized predictive control method based on Kalman filter observer.On the basis of generalized predictive control,a state observer based on Kalman filter is added,and the data observed by Kalman filter is used to replace the measured control quantity to calculate the deviation of generalized predictive control.In addition,the shrinkage factor of the accuracy error of the Kalman filter model is introduced to prevent the Kalman filter observer from falling into the static error trap,so as to effectively suppress the fluctuation and even oscillation caused by the model mismatch,and eliminate the static error.

关 键 词：卡尔曼滤波 预测控制 模型失配 收缩因子 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置] TN713[自动化与计算机技术—控制科学与工程]