Optimal and robust full-order smoothers for linear descriptor systems
DOU Yin-feng
SUN Shu-li
RAN Chen-jian
Abstract：For the linear discrete stochastic descriptor systems, the smoothing problem has been transformed to the filtering problem of one augmented state. Based on the maxinum likelihood(ML)linear estimation criterion,the optimal full-order smoothers are presented, where the filtering error variance of the augmented state is presented based on the descriptor Riccati equation. When the variances of the process noise and the measurement noise of the descriptor systems are uncertain,robust full-order smoothers are obtained based on the max-min robust design theory and the optimal full-order smoothing algorithm. Applying the dynamic error variance analysis (DEVA) method, the robustness is proved, i.e.,the variance matrices of the robust smoothers have upper bound variance matrices. Simulation example verifies the effectiveness.
Keywords：descriptor systemsoptimal full-order smoothersrobust full-order smoothersrobustnessdynamic error variance analysis(DEVA)method
Publication Date：2018-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：8( 207-214 )