This article is concerned with the robust set-membership fault estimation problem for a class of uncertain discrete time-varying systems over sensor networks. The system measurements are subject to the uniform quantization which results in the unknown-but-bounded noises. Attention is focused on the design of a set-membership fault estimator such that, in the simultaneous presence of uncertain parameters, unknown-but-bounded noises and uniform quantization effects, the estimation errors are confifined to a certain ellipsoidal region. By using the mathematical induction, a suffificient condition is derived for the existence of the desired fault estimator at each time step in terms of a set of recursive matrix inequalities. Moreover, an optimization problem is formulated by minimizing the ellipsoid of the estimation error. Finally, two numerical examples are provided to illustrate the effectiveness of the proposed fault estimator design scheme.
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http://dx.doi.org/10.7717/peerj-cs.872 | DOI Listing |
This article is concerned with the robust set-membership fault estimation problem for a class of uncertain discrete time-varying systems over sensor networks. The system measurements are subject to the uniform quantization which results in the unknown-but-bounded noises. Attention is focused on the design of a set-membership fault estimator such that, in the simultaneous presence of uncertain parameters, unknown-but-bounded noises and uniform quantization effects, the estimation errors are confifined to a certain ellipsoidal region.
View Article and Find Full Text PDFIEEE Trans Neural Netw Learn Syst
February 2023
In this article, the simultaneous state and fault estimation problem is investigated for a class of nonlinear 2-D shift-varying systems, where the sensors and the estimator are connected via a communication network of limited bandwidth. With the purpose of relieving the communication burden and enhancing the transmission security, a new encoding-decoding mechanism is put forward so as to encode the transmitted data with a finite number of bits. The aim of the addressed problem is to develop a neural-network (NN)-based set-membership estimator for jointly estimating the system states and the faults, where the estimation errors are guaranteed to reside within an optimized ellipsoidal set.
View Article and Find Full Text PDFThis article is concerned with set-membership global estimation for a networked system under unknown-but-bounded process and measurement noises. First, a group of local set-membership estimators is deployed to obtain the local ellipsoidal estimate of the true system state. Each estimator is capable of communicating with its neighbors within its communication range.
View Article and Find Full Text PDFISA Trans
November 2019
Navigation and Control Research Center, Department of Automation, Tsinghua University, 100084 Beijing, PR China.
This paper proposes a robust state and fault estimation (SFE) method for discrete-time descriptor linear-parameter-varying (LPV) systems with inexact scheduling variables. As an important robust method dealing with system uncertainties, the set-membership estimation method is combined with the technique of generalized fault detectability indices and matrix to compute a state and fault tube to contain the real system states and fault signals at each time instant under the assumption that the system uncertainties (i.e.
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