Iterative learning scheme to design intermittent fault estimators for nonlinear systems with parameter uncertainties and measurement noise

Abstract

In this paper, an iterative learning estimator is proposed to deal with period intermittent fault estimation problem in a class of nonlinear uncertain systems. First, state observer is designed for state reconstruction, followed by the Lyapunov function is presented to guarantee the convergence of the system output. Then, the iterative learning scheme-based fault estimator is presented to track the fault signal and the optimal function is established to ensure tracking error convergence. Moreover, linear matrix inequalities and Schur complements are utilized to obtain the sufficient conditions for the existence of iterative learning estimator. Compared with the existing results, error augmented systems should not satisfy the strictly positive realness assumption. Besides, previous state estimation error is used for current fault estimation such that to improve the estimating accuracy. Finally, 2 numerical examples are given to illustrate the effectiveness and validity of the proposed methods. KEYWORDS fault estimation, intermittent fault, iterative learning scheme, LMI, nonlinear uncertain systems 994

Fault detection and identification for rolling mill main drive system based on integrated observer under iterative learning strategy

“…Therefore, this thesis aims to design a nonlinear observer to solve the fault diagnosis problem of the rolling mill MDS. It is worth noting that the fault reconfiguration scheme proposed in this study is an improvement on the method of Feng et al 31 We improve this method by combining an UIO with an IL algorithm. In addition, the isolation of multiple faults was not considered in the study by Feng et al 31 In response to the problem that the system is affected by external disturbances and multiple faults, an observer-based system fault detection, isolation and reconfiguration scheme is proposed, and the feasibility of its application to the rolling mill MDS is investigated.…”

“…He and Wu 30 introduced an iterative learning (IL) algorithm and designed an IL disturbance observer, which has the advantage of high accuracy of disturbance estimation. Feng et al 31 studied the fault estimation problem of nonlinear systems with parameter uncertainty using IL observers. Chen et al 32 proposed a method combining IL observers and filters to solve the fault estimation problem of generalised linear systems with external disturbances.…”

“…Therefore, this thesis aims to design a nonlinear observer to solve the fault diagnosis problem of the rolling mill MDS. It is worth noting that the fault reconfiguration scheme proposed in this study is an improvement on the method of Feng et al 31 We improve this method by combining an UIO with an IL algorithm. In addition, the isolation of multiple faults was not considered in the study by Feng et al 31…”

Multi-fault diagnosis scheme based on robust nonlinear observer with application to rolling mill main drive system

“…Unlike the conventional SMO, this method does not use the equal output injected method to reconstruct the fault and solve the jitter problem. Feng et al (2018) studied the problem of system fault identification based on iterative learning observer and combined the iterative learning algorithm to track the fault signal, which improved the accuracy of fault estimation, but this method also needed prior knowledge of known faults. Through the above analysis, these studies need to be improved on the issue that the upper bound of fault signal needs to be known.…”

An Iterative Learning Scheme‐Based Fault Estimator Design for Nonlinear Systems with Randomly Occurring Parameter Uncertainties

“…Motivated by predictive and iterative learning control theories, the fault tracking approximator uses iterative algorithms to detect and identify nonlinear system faults, even in the presence of model uncertainty [25]. The latest work of the iterative learning schemebased fault estimation observer is designed for multiphase batch processes with delays, disturbances, and actuator faults [26,27]; a class of differential time-delay batch processes with actuator faults [28,29]; and nonlinear systems with randomly changed trial length, period intermittent fault, and time delay [30][31][32]. Unfortunately, to the best of the authors' knowledge, the iterative learning schemebased fault estimation problem has not been fully investigated, not to mention the case where the systems also involve parameter uncertainties.…”