Displaying similar documents to “Unbiased minimum-variance filter for state and fault estimation of linear time-varying systems with unknown disturbances.”

Novel optimal recursive filter for state and fault estimation of linear stochastic systems with unknown disturbances

Karim Khémiri, Fayçal Ben Hmida, José Ragot, Moncef Gossa (2011)

International Journal of Applied Mathematics and Computer Science

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This paper studies recursive optimal filtering as well as robust fault and state estimation for linear stochastic systems with unknown disturbances. It proposes a new recursive optimal filter structure with transformation of the original system. This transformation is based on the singular value decomposition of the direct feedthrough matrix distribution of the fault which is assumed to be of arbitrary rank. The resulting filter is optimal in the sense of the unbiased minimum-variance...

Simultaneous state and parameter estimation based actuator fault detection and diagnosis for an unmanned helicopter

Chong Wu, Juntong Qi, Dalei Song, Xin Qi, Jianda Han (2015)

International Journal of Applied Mathematics and Computer Science

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Simultaneous state and parameter estimation based actuator fault detection and diagnosis (FDD) for single-rotor unmanned helicopters (UHs) is investigated in this paper. A literature review of actuator FDD for UHs is given firstly. Based on actuator healthy coefficients (AHCs), which are introduced to represent actuator faults, a combined dynamic model is established with the augmented state containing both the flight state and AHCs. Then the actuator fault detection and diagnosis problem...

A mixed active and passive GLR test for a fault tolerant control system

Hicham Jamouli, Mohamed Amine El Hail, Dominique Sauter (2012)

International Journal of Applied Mathematics and Computer Science

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This paper presents an adaptive Generalized Likelihood Ratio (GLR) test for multiple Faults Detection and Isolation (FDI) in stochastic linear dynamic systems. Based on the work of Willsky and Jones (1976), we propose a modified generalized likelihood ratio test, allowing detection, isolation and estimation of multiple sequential faults. Our contribution aims to maximise the good decision rate of fault detection using another updating strategy. This is based on a reference model updated...

Robust sensor fault estimation for descriptor-LPV systems with unmeasurable gain scheduling functions: application to an anaerobic bioreactor

Francisco-Ronay López-Estrada, Jean-Christophe Ponsart, Didier Theilliol, Carlos-Manuel Astorga-Zaragoza, Jorge-Luis Camas-Anzueto (2015)

International Journal of Applied Mathematics and Computer Science

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This paper addresses the design of a state estimation and sensor fault detection, isolation and fault estimation observer for descriptor-linear parameter varying (D-LPV) systems. In contrast to where the scheduling functions depend on some measurable time varying state, the proposed method considers the scheduling function depending on an unmeasurable state vector. In order to isolate, detect and estimate sensor faults, an augmented system is constructed by considering faults to be auxiliary...

Neural network-based fault diagnosis and fault-tolerant control for nonlinear systems with output measurement noise

Yanjun Shen, Chen Ma, Chenhao Zhao, Zebin Wu (2024)

Kybernetika

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In this article, the problems of fault diagnosis (FD) and fault-tolerant control (FTC) are investigated for a class of nonlinear systems with output measurement noise. Due to the influence of measurement noise in the output sensor, the output observation error cannot be accurately obtained, which causes obstacles to the accuracy of FD. To address this issue, an output filter and disturbance estimator are constructed to decrease the negative effects of measurement noise and observer gain...

Fault diagnosis in a networked control system under communication constraints: a quadrotor application

Karim Chabir, Mohamed Amine Sid, Dominique Sauter (2014)

International Journal of Applied Mathematics and Computer Science

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This paper considers the problem of attitude sensor fault diagnosis in a quadrotor helicopter. The proposed approach is composed of two stages. The first one is the modelling of the system attitude dynamics taking into account the induced communication constraints. Then a robust fault detection and evaluation scheme is proposed using a post-filter designed under a particular design objective. This approach is compared with previous results based on the standard Kalman filter and gives...

Robust multisensor fault tolerant model-following MPC design for constrained systems

Alain Yetendje, Maria M. Seron, José A. De Doná (2012)

International Journal of Applied Mathematics and Computer Science

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In this paper, a robust fault-tolerant control strategy for constrained multisensor linear systems, subject to sensor faults and in the presence of bounded state and output disturbances, is proposed. The scheme verifies that, for each sensors-estimator combination, suitable residual variables lie inside pre-computed sets and selects a more appropriate combination based on a chosen criterion. An active fault tolerant output feedback controller yields an MPC-based control law and, by means...

Switching time estimation and active mode recognition using a data projection method

Assia Hakem, Vincent Cocquempot, Komi Midzodzi Pekpe (2016)

International Journal of Applied Mathematics and Computer Science

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This paper proposes a data projection method (DPM) to detect a mode switching and recognize the current mode in a switching system. The main feature of this method is that the precise knowledge of the system model, i.e., the parameter values, is not needed. One direct application of this technique is fault detection and identification (FDI) when a fault produces a change in the system dynamics. Mode detection and recognition correspond to fault detection and identification, and switching...

Fault detection and isolation with robust principal component analysis

Yvon Tharrault, Gilles Mourot, José Ragot, Didier Maquin (2008)

International Journal of Applied Mathematics and Computer Science

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Principal component analysis (PCA) is a powerful fault detection and isolation method. However, the classical PCA, which is based on the estimation of the sample mean and covariance matrix of the data, is very sensitive to outliers in the training data set. Usually robust principal component analysis is applied to remove the effect of outliers on the PCA model. In this paper, a fast two-step algorithm is proposed. First, the objective was to find an accurate estimate of the covariance...

An H ∞ sliding mode observer for Takagi-Sugeno nonlinear systems with simultaneous actuator and sensor faults

Ali Ben Brahim, Slim Dhahri, Fayçal Ben Hmida, Anis Sellami (2015)

International Journal of Applied Mathematics and Computer Science

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This paper considers the problem of robust reconstruction of simultaneous actuator and sensor faults for a class of uncertain Takagi-Sugeno nonlinear systems with unmeasurable premise variables. The proposed fault reconstruction and estimation design method with H∞ performance is used to reconstruct both actuator and sensor faults when the latter are transformed into pseudo-actuator faults by introducing a simple filter. The main contribution is to develop a sliding mode observer (SMO)...

Actuator fault diagnosis for flat systems: A constraint satisfaction approach

Ramatou Seydou, Tarek Raissi, Ali Zolghadri, Denis Efimov (2013)

International Journal of Applied Mathematics and Computer Science

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This paper describes a robust set-membership-based Fault Detection and Isolation (FDI) technique for a particular class of nonlinear systems, the so-called flat systems. The proposed strategy consists in checking if the expected input value belongs to an estimated feasible set computed using the system model and the derivatives of the measured output vector. The output derivatives are computed using a numerical differentiator. The set-membership estimator design for the input vector...