讨论了线性离散奇异系统的状态估计问题。
State estimation of singular discrete-time linear systems is discussed.
首先分析总结了线性离散变系数奇异系统可解性及其广义状态解的一般概念。
First, the general notion of solvability and generalized state solutions for linear discrete coefficient_vary singular systems are analyzed.
给出了一类离散滞后不确定奇异系统的鲁棒d -稳定性条件，发现渐近稳定的条件与时滞无关。
In this paper, a sufficient condition of robust D-stability for discrete-delay uncertain singular systems is presented.
讨论含参数不确定的离散时变时滞奇异系统的时滞相关的鲁棒状态反馈稳定化问题。
The robust stabilization via state feedback for time-varying delays discrete-time singular systems with parameter uncertainties is discussed.
研究了一类不确定离散切换线性奇异（SLS）系统任意切换律下的鲁棒容许性问题。
The robust admissibility analysis of a class of uncertain discrete-time switched linear singular(SLS)systems for arbitrary switching laws is addressed.
本文提出一种新的广义李亚普诺夫方程，用于判定离散时间奇异系统的稳定性。
This paper puts forward a kind of new generalized Lyapunuv equation, which is used to study the stability of discrete singular system.
建立了线性定常离散互联电力系统的奇异摄动模型。
A constant linear discrete time interconnected stochastic power system is represented in a singularly perturbed form.
讨论隐含离散时间奇异非线性系统的精确线性化问题。
The exact linearization of a class of implicit discrete-time nonlinear singular systems is studied.
利用群逆可以求得离散型对称奇异系统的显解。
Using the group inverse, an explicit solution to a symmetric singular system is described.
基于多项式ARMA新息模型方法提出了随机奇异线性离散时间系统的稳态最优估计。
The steady-state optimal estimation of singular systems is studied by applying ARMA innovation model.
利用广义逆理论和奇异值分解理论，研究离散型线性随机系统的综合控制设计问题。
This paper discusses the synthetical control designing problem for discrete linear stochastic systems with generalized inverse theory and the singular value decomposition theory.
对于带多传感器的广义线性离散随机系统，基于奇异值分解，将其化为等价的两个降阶多传感器子系统。
For the linear discrete stochastic descriptor systems with multisensor, based on the singular value decomposition, the equivalent two reduced order multisensor subsystems are obtained.
对于带多传感器的广义线性离散随机系统，基于奇异值分解，将其化为等价的两个降阶多传感器子系统。
For the linear discrete stochastic descriptor systems with multisensor, based on the singular value decomposition, the equivalent two reduced order multisensor subsystems are obtained.
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