摘要
应用时域上的现代时间序列分析方法 ,基于自回归滑动平均 (ARMA)新息模型和白噪声估值器 ,应用控制理论中的极点配置原理 ,对线性离散时间广义随机系统提出了极点配置广义稳态Kalman估值器 .它们具有全局渐近稳定性 ,且通过配置估值器的极点可按指数衰减速率使初始状态估值的影响快速消失 .它们可在统一框架下处理滤波、平滑和预报问题 .它们避免了Ric cati方程和最优初始状态估值的计算 ,因而可减小计算负担 .
Using the modern time-series analysis method in the time domain, based on the autoregressive moving average (ARMA) innovation model and white noise estimators, and in terms of the pole-assignment principle in control theory, the pole-assignment descriptor steady-state Kalman estimators are presented for linear discrete-time descriptor stochastic systems. They have globally asymptotic stability, and can forget the effect of the initial state estimates at an exponentially decaying rate by assigning the poles of the estimators. They can handle the filtering, smoothing and prediction problems in a unified framework. They avoid the Riccati equation and the computation of the optimal initial state estimates so that reduce the computational burden. A simulation example shows their effectiveness.
出处
《自动化学报》
EI
CSCD
北大核心
2003年第6期835-841,共7页
Acta Automatica Sinica
基金
SupportedbyNationalNaturalScienceFoundationofP .R .China (6 9774 0 19) ,NaturalScienceFoundationofHei longjiangProvince (F0 1 15 ) ,andtheFoundationofBeijingMunicipalEducationCommission
关键词
控制理论
极点配置
广义稳态Kalman估值器
时间序列分析方法
时域
Computer simulation
Control theory
Discrete time control systems
Estimation
Linear control systems
Mathematical models
Theorem proving
Time series analysis
