A continuous-discrete finite memory observer design for a class of nonlinear systems: application to fault diagnosis
type: Item
instance of: scholarly article
title: A continuous-discrete finite memory observer design for a class of nonlinear systems: application to fault diagnosis
zbMATH Open document ID: 1459.93059
DOI: 10.1155/2020/7312521
author: Frédéric Kratz
author: Yunhui Hou
author: Vincent Idasiak
author: Ting-Ting Zhang
published in: Mathematical Problems in Engineering
publication date: 2020-07-30T00:00:00Z
review text: Summary: This paper aims to develop a continuous-discrete finite memory observer (CD-FMO) for a class of nonlinear dynamical systems modeled by ordinary differential equations (ODEs) with discrete measurements. The nonlinear systems under consideration are at least \textit{locally} Lipschitz, which guarantees the existence and uniqueness of solution at each time instant. The proposed nonlinear observer uses a finite number of collected measurements to estimate the system state in the presence of measurement noise. Besides, a one-step prediction algorithm incorporated with an iterative-update scheme is performed to solve the integral problem caused by system nonlinearity, and an analysis of the numerical integration approximation error is given. The properties of estimation performance have been further proved in deterministic case and been analyzed by Monte Carlo simulation in stochastic cases. It is worth noting that the presented method has a finite-time convergence, while most nonlinear observers are usually asymptotically convergent. Another advantage of CD-FMO is that it has no initial value problem. For the application purpose, residuals are generated to implement fault detection cooperated with Cumulative Sum (CUSUM) control charts, while a bank of CD-FMOs is adopted to realize fault isolation for different sensor and actuator faults of the considered nonlinear robotic arm. The robustness and effectiveness of the proposed approach are illustrated via the simulation results.
Mathematics Subject Classification ID: 93B53
Mathematics Subject Classification ID: 93B07
Mathematics Subject Classification ID: 93C10
Mathematics Subject Classification ID: 93C43
Mathematics Subject Classification ID: 93C73
Mathematics Subject Classification ID: 94C12
zbMATH DE Number: 7226158
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2020/7312521
OpenAlex ID: W3043049512
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description: scientific article; zbMATH DE number 7226158