Displaying similar documents to “Fault estimation for time-varying systems with Round-Robin protocol”

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)...

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...

Systematic fault tolerant control based on adaptive Thau observer estimation for quadrotor UAVs

Zhaohui Cen, Hassan Noura, Younes Al Younes (2015)

International Journal of Applied Mathematics and Computer Science

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A systematic fault tolerant control (FTC) scheme based on fault estimation for a quadrotor actuator, which integrates normal control, active and passive FTC and fault parking is proposed in this paper. Firstly, an adaptive Thau observer (ATO) is presented to estimate the quadrotor rotor fault magnitudes, and then faults with different magnitudes and time-varying natures are rated into corresponding fault severity levels based on the pre-defined fault-tolerant boundaries. Secondly, a...

A Model-Based Fault Detection and Diagnosis Scheme for Distributed Parameter Systems: A Learning Systems Approach

Michael A. Demetriou (2010)

ESAIM: Control, Optimisation and Calculus of Variations

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In this note, fault detection techniques based on finite dimensional results are extended and applied to a class of infinite dimensional dynamical systems. This special class of systems assumes linear plant dynamics having an abrupt additive perturbation as the fault. This fault is assumed to be linear in the (unknown) constant (and possibly functional) parameters. An observer-based model estimate is proposed which serves to monitor the system's dynamics for unanticipated failures, and...

Detection and identification of loss of efficiency faults of flight actuators

Daniel Ossmann, Andreas Varga (2015)

International Journal of Applied Mathematics and Computer Science

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We propose linear parameter-varying (LPV) model-based approaches to the synthesis of robust fault detection and diagnosis (FDD) systems for loss of efficiency (LOE) faults of flight actuators. The proposed methods are applicable to several types of parametric (or multiplicative) LOE faults such as actuator disconnection, surface damage, actuator power loss or stall loads. For the detection of these parametric faults, advanced LPV-model detection techniques are proposed, which implicitly...

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...

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...

Robust fault detection of singular LPV systems with multiple time-varying delays

Amir Hossein Hassanabadi, Masoud Shafiee, Vicenç Puig (2016)

International Journal of Applied Mathematics and Computer Science

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In this paper, the robust fault detection problem for LPV singular delayed systems in the presence of disturbances and actuator faults is considered. For both disturbance decoupling and actuator fault detection, an unknown input observer (UIO) is proposed. The aim is to compute a residual signal which has minimum sensitivity to disturbances while having maximum sensitivity to faults. Robustness to unknown inputs is formulated in the sense of the H∞ -norm by means of the bounded real...

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...