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

Li Feng,Shuiqing Xu,Ke Zhang,Yi Chai,Zhimin Yang

doi:10.1155/2017/1850737

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

Highlights

Due to the increasing demand for reliability and safety and acceptable performance of control engineering systems, faulttolerant control [1,2,3,4] and fault detection [5, 6] have become an attractive theory and application topic in the last decades
Many researchers pay a lot of attention on iterative learning scheme-based fault estimation design [21,22,23]
The paper [23] proposes a novel fault estimation algorithm based on iterative learning scheme which is presented for a class of timevarying discrete switched systems with arbitrary sequence

Summary

Introduction

Due to the increasing demand for reliability and safety and acceptable performance of control engineering systems, faulttolerant control [1,2,3,4] and fault detection [5, 6] have become an attractive theory and application topic in the last decades. The aforementioned problems motivate the work in this paper: a novel iterative learning scheme-based fault estimator is designed to consider fault estimation problem for nonlinear systems with randomly changed trial length. (2) By using average factor, a novel iterative learning method is proposed for the first time to track the fault signal and to decrease the effect of lack and redundancy caused by randomly changed length. (3) The proposed method inherits the advantages of conventional iterative learning scheme and the bounds of the faults and their derivatives could be unknown That is, it can overcome the problem of randomly changed trial lengths without using advanced algorithms such as neural network and fuzzy system.

Problem Formulation

Iterative Learning Scheme-Based Fault Estimation Design

Convergence Analysis

Illustrative Example

Conclusion