Iterative learning fault estimation design for nonlinear system with random trial length
type: Item
instance of: scholarly article
title: Iterative learning fault estimation design for nonlinear system with random trial length
zbMATH Open document ID: 1380.93245
DOI: 10.1155/2017/1850737
author: Peng Zhang
published in: Complexity
publication date: 2018-02-01T00:00:00Z
review text: Summary: An iterative learning scheme-based fault estimation observer is designed for a class of nonlinear systems with randomly changed trial length. This is achieved by presenting a state observer for monitoring the system state and an iterative learning law for fault estimation in the presence of imprecise system model. An average factor is defined to deal with the lack and redundancy in tracking information caused by random trial length. Via the convergence analysis, sufficient design conditions are developed for estimation of fault signal. The observer gains and iterative learning law indexes are computed by solving the proposed conditions under \(\lambda\)-\textit{norm} constraints. Numerical examples are presented to demonstrate the validity, the effectiveness, and the superiority of this method.
Mathematics Subject Classification ID: 93E10
Mathematics Subject Classification ID: 93B07
Mathematics Subject Classification ID: 93E03
Mathematics Subject Classification ID: 93C10
zbMATH DE Number: 6833370
zbMATH Keywords: iterative learning fault estimation design
zbMATH Keywords: nonlinear system with random trial length
zbMATH Keywords: state observer
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2017/1850737
OpenAlex ID: W2769206081
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description: scientific article; zbMATH DE number 6833370