UNSCENTED KALMAN FILTERING FOR GREENHOUSE CLIMATE CONTROL SYSTEMS WITH MISSING MEASUREMENT

被引：0
|
作者：
Luan, Xiaoli [2 ]
Shi, Yan [1 ]
Liu, Fei [2 ]
机构：
[1] Tokai Univ, Sch Ind Engn, Kumamoto 8628652, Japan
[2] Jiangnan Univ, Key Lab Adv Proc Control Light Ind, Minist Educ, Inst Automat, Wuxi 214122, Peoples R China
来源：
INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL | 2012年 / 8卷 / 3B期
基金：
英国工程与自然科学研究理事会; 中国国家自然科学基金;
关键词：
Unscented Kalman filter; Greenhouse; Nonlinear systems; State estimation; MODEL;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A stochastic unscented Kalman filter is designed in an attempt to solve the state estimation problem of the greenhouse climate control systems with missing measurements. The missing measurements are described by a binary switching sequence satisfying a conditional probability distribution. In order to accommodate the effects of randomly varying arrival of measurement data, the stochastic unscented transformation coupled with certain parts of the classic Kalman filter is applied to estimate the greenhouse states and filter out the noises, where some or all measurements are lost in a random fashion. The simulation results demonstrate the performance degradation of state estimation caused by random measurement data loss.
引用
收藏
页码：2173 / 2180
页数：8
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