Robust state estimation for uncertain linear systems with random parametric uncertainties

被引：12

作者：

Liu, Huabo ^{[1
,2
]}

Zhou, Tong ^{[1
,3
]}

机构：

[1] Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China

[2] Qingdao Univ, Coll Automat & Elect Engn, Qingdao 266071, Peoples R China

[3] Tsinghua Univ, Tsinghua Natl Lab Informat Sci & Technol TNList, Beijing 100084, Peoples R China

来源：

SCIENCE CHINA-INFORMATION SCIENCES | 2017年 / 60卷 / 01期

基金：

中国国家自然科学基金;

关键词：

robustness; state estimation; recursive estimation; parametric uncertainty; regularized least-squares; DESIGN;

D O I：

10.1007/s11432-015-0327-x

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, we investigate state estimations of a dynamical system with random parametric uncertainties which may arbitrarily affect a plant state-space model. A robust estimator is derived based on expectation minimization of estimation errors. An analytic solution similar to that of the well-known Kalman filter is derived for this new robust estimator which can be realized recursively with a comparable computational complexity. Under some weak assumptions, it is proved that this estimator converges to a stable system, the covariance matrix of estimation errors is bounded, and the estimation is asymptotically unbiased. Numerical simulations show that the obtained robust filter has an estimation accuracy comparable to other robust estimators and can be applied in a wider range.

引用

页数：13