Robust state estimation using desensitized Divided Difference Filter

被引：33

作者：

Karlgaard, Christopher D. ^{[1
]}

Shen, Haijun ^{[1
]}

机构：

[1] Analyt Mech Associates Inc, Hampton, VA 23666 USA

来源：

ISA TRANSACTIONS | 2013年 / 52卷 / 05期

关键词：

Robust filtering; Optimal filtering; Divided difference filtering; Parameter uncertainty; UNSCENTED KALMAN FILTER; COVARIANCE WHITE NOISES; DISCRETE-TIME-SYSTEMS; INDUCTION-MOTOR; NONLINEAR-SYSTEMS; OPTIMAL TRAJECTORIES; PARAMETER-ESTIMATION; VARYING SYSTEMS; RESISTANCE; ALGORITHM;

D O I：

10.1016/j.isatra.2013.04.009

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper develops a robust divided difference filtering approach based on the concept of Desensitized Kalman Filtering. The filters are formulated using a minimum variance cost function, augmented with a penalty function consisting of a weighted norm of the state sensitivities. Solutions are provided for first and second-order Divided Difference Filters. The resulting filters are non-minimum variance but exhibit reduced sensitivity to deviations in the assumed plant model parameters. The proposed algorithms are demonstrated using Monte Carlo simulation techniques for an induction motor state estimation problem with parameter uncertainties. (C) 2013 ISA. Published by Elsevier Ltd. All rights reserved.

引用

页码：629 / 637

页数：9

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