Iterative Learning Fault Estimation Design for Nonlinear System with Random Trial Length
Joint Authors
Shuiqing, Xu
Ke, Zhang
Li, Feng
Yang, Zhimin
Yi, Chai
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-11-23
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
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 λ-norm constraints.
Numerical examples are presented to demonstrate the validity, the effectiveness, and the superiority of this method.
American Psychological Association (APA)
Li, Feng& Ke, Zhang& Yi, Chai& Shuiqing, Xu& Yang, Zhimin. 2017. Iterative Learning Fault Estimation Design for Nonlinear System with Random Trial Length. Complexity،Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1142584
Modern Language Association (MLA)
Li, Feng…[et al.]. Iterative Learning Fault Estimation Design for Nonlinear System with Random Trial Length. Complexity No. 2017 (2017), pp.1-9.
https://search.emarefa.net/detail/BIM-1142584
American Medical Association (AMA)
Li, Feng& Ke, Zhang& Yi, Chai& Shuiqing, Xu& Yang, Zhimin. Iterative Learning Fault Estimation Design for Nonlinear System with Random Trial Length. Complexity. 2017. Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1142584
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1142584