Data-driven fault estimation of non-minimum phase LTI systems
type: Item
instance of: scholarly article
title: Data-driven fault estimation of non-minimum phase LTI systems
zbMATH Open document ID: 1388.93089
author: Chengpu Yu
author: Michel Verhaegen
published in: Automatica
publication date: 2018-06-20T00:00:00Z
Mathematics Subject Classification ID: 93E10
Mathematics Subject Classification ID: 93B30
Mathematics Subject Classification ID: 93C05
zbMATH DE Number: 6891947
zbMATH Keywords: fault estimation
zbMATH Keywords: data-driven methods
zbMATH Keywords: non-minimum phase systems
zbMATH Keywords: subspace identification
MaRDI profile type: Publication
full work available at URL: j.automatica.2018.03.035
OpenAlex ID: W2794428491
cites work: The role of vector autoregressive modeling in predictor-based subspace identification
cites work: Identification of Fault Estimation Filter From I/O Data for Systems With Stable Inversion
cites work: Discrete-Time Linear Systems
cites work: Optimal filtering for systems with unknown inputs
cites work: Fast projection methods for minimal design problems in linear system theory
cites work: \(l\)-delay input and initial-state reconstruction for discrete-time linear systems
cites work: Q4524141
cites work: Unknown-state, unknown-input reconstruction in discrete-time nonminimum-phase systems: geometric methods
cites work: Q3998482
cites work: Filtering and System Identification
cites work: Data-driven robust receding horizon fault estimation
Wikidata QID: Q130066042
DOI: 10.1016/J.AUTOMATICA.2018.03.035
description: scientific article; zbMATH DE number 6891947