A NEW RECURSIVE FORMULATION FOR FAST ESTIMATION OF TIME-VARYING UNKNOWN INPUT. APPLICATION FOR ESTIMATING A PARTICLE ACCELERATION

被引：0

作者：

Sircoulomb, Vincent ^{[1
]}

Hoblos, Ghaleb ^{[1
]}

Chafouk, Houcine ^{[1
]}

Ragot, Jose ^{[2
]}

机构：

[1] Inst Rech Syst Elect EMbarques, EA 4353, F-76801 St Etienne, France

[2] Inst Natl Polytech Lorraine, CRAN, CNRS, UMR 7039, F-54516 Vandoeuvre Les Nancy, France

来源：

INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL | 2011年 / 7卷 / 7A期

关键词：

Unknown input estimation; Kalman filtering; Least squares; Recursive algorithm; Integral effect; Computation; Tracking; VARIABLE-DIMENSION FILTER; KALMAN FILTER; LINEAR-SYSTEMS; TRACKING; PERFORMANCE; INVERSION; ALGORITHM; DESIGN;

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：

This paper proposes an extension of the Recursive Input Estimation (RIE) for estimating time-varying unknown input. This extension is based on modifications of the Least Squares (LS) algorithm of the RIE, which increase its capability to track time varying unknown input. These modifications consist in inserting a forgetting factor into the LS algorithm and adding an integral effect. Moreover, alternative formulations are proposed, allowing the reduction of computation time in the case where the number of inputs to estimate is lower than the number of measurements. The different tools obtained are tested and compared on a three-dimensional tracking application.

引用

页码：3779 / 3797

页数：19

共 9 条

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