Sensor fault and state estimation for continuous-time descriptor linear systems: An LMI approach

Brahim, Imen Haj; Chaabane, Mohamed; Hashim, Ghani; Mehdi, Driss

doi:10.1109/icosc.2016.7507057

Cited by 5 publications

(3 citation statements)

References 29 publications

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“…In this perspective, the aim of this work is to study the problem of observer design in order to propose actuator, and sensor faults estimation method for a class of discrete-time descriptor linear models.This problem has been widely tackled and many research works are devoted to this class of systems. They relate to explicit and singular linear models in both continuous and discrete-time cases [30][31][32][33][34][35][36][37][38][39][40]. The resolution of the design and stability problems of the observer lies in the writing of the convergence conditions in the form of LMIs solved by a dedicated software [41].…”

Section: Introductionmentioning

confidence: 99%

Observer Design for Simultaneous State and Fault Estimation for a Class of Discrete-time Descriptor Linear Models

et al. 2021

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This paper investigates the problem of observer design for simultaneous states and faults estimation for a class of discrete-time descriptor linear models in presence of actuator and sensor faults. The idea of the present result is based on the second equivalent form of implicit model [1] which permits to separate the differential and algebraic equations in the considered singular model, and the use of an explicit augmented model structure. At that stage, an observer is built to estimate simultaneously the unknown states, the actuator faults, and the sensor faults. Next, the explicit structure of the augmented model is established. Then, an observer is built to estimate simultaneously the unknown states, the actuator faults, and the sensor faults. By using the Lyapunov approach, the convergence of the state estimation error of the augmented system is analyzed, and the observer’s gain matrix is achieved by solving only one linear matrix inequality (LMI). At long last, an illustrative model is given to show the performance and capability of the proposed strategy.

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Section: Introductionmentioning

confidence: 99%

Observer Design for Simultaneous State and Fault Estimation for a Class of Discrete-time Descriptor Linear Models

et al. 2021

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“…They concern explicit and singular linear models in both continuous and discrete time cases see e.g. [11][12][13][14][15][16][17][18][19][20][21][22].…”

Section: Introduction and Problem Statement 1introductionmentioning

confidence: 99%

State and Unknown Input Simultaneous Estimation for a Class of Discrete-Time Linear Implicit Models : A Heat Exchanger Pilot Process Application

et al. 2021

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In this paper, the design problem of simultaneous estimation of unmeasurable states and unknown inputs (UIs) is investigated for a class of discrete-time linear implicit models (DLIMs). The UIs affect both state and output of the system. The approach is based on the separation between dynamic and static relations in the considered DLDM. First, the method permitting to separate dynamic equations from static equations is exposed. Next, an augmented explicit model which contains the dynamic equations and the UIs is constructed. Then an unknown inputs observer (UIO) design in explicit structure is developed. The exponential convergence of the state estimation error is studied by using the Lyapunov theory and the stability condition is given in term of linear matrix inequality (LMI). Finally, an illustrative application of a heat exchanger pilot process is given to show the good performances of the proposed method.

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“…In the domain of fault estimation for the linear descriptor systems, a robust fault estimation observer was designed in Brahim et al (2016). For a Lipschitz non-linear descriptor model, Wang et al (2014) proposed a novel structure robust observer for fault estimation.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Kalman filtering for sensor fault estimation and isolation of satellite attitude control based on descriptor systems

Wang

Tao

2019

Transactions of the Institute of Measurement and Control

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Sensor fault estimation and isolation is significant for an attitude control systems model of a satellite, as it works in a complex environment. The standard unscented Kalman filter algorithm may lose its accuracy when the noise is considerable. Therefore, an adaptive filtering algorithm is proposed based on the sampled-data descriptor model. The performance of the unscented Kalman filter in sensor fault estimation is improved by the adaptive algorithm depending on innovation and the measurement residual, and its convergence is guaranteed. Combining the adaptive unscented Kalman filter with the multiple-model adaptive estimation, a sensor fault isolation method is proposed. Finally, simulation examples show that this algorithm has better estimating accuracy and isolation results.

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Sensor fault and state estimation for continuous-time descriptor linear systems: An LMI approach

Cited by 5 publications

References 29 publications

Observer Design for Simultaneous State and Fault Estimation for a Class of Discrete-time Descriptor Linear Models

Observer Design for Simultaneous State and Fault Estimation for a Class of Discrete-time Descriptor Linear Models

State and Unknown Input Simultaneous Estimation for a Class of Discrete-Time Linear Implicit Models : A Heat Exchanger Pilot Process Application

Adaptive Kalman filtering for sensor fault estimation and isolation of satellite attitude control based on descriptor systems

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