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Complexity
Volume 2017, Article ID 1850737, 9 pages
https://doi.org/10.1155/2017/1850737
Research Article

Iterative Learning Fault Estimation Design for Nonlinear System with Random Trial Length

College of Automation, Chongqing University, Chongqing 400044, China

Correspondence should be addressed to Ke Zhang; moc.361@atems

Received 11 July 2017; Revised 13 September 2017; Accepted 24 September 2017; Published 23 November 2017

Academic Editor: Michele Scarpiniti

Copyright © 2017 Li Feng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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 - constraints. Numerical examples are presented to demonstrate the validity, the effectiveness, and the superiority of this method.