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This paper studies the problem of sensor fault interval estimation for discrete-time linear system with unknown but bounded disturbances and noises. A descriptor system which is equivalent to the original system is constructed by considering the fault as an auxiliary state vector. Based on the augmented descriptor system, a zonotopic Kalman filter is designed to obtain the interval estimation of sensor fault. To improve estimation accuracy, we further propose a Frobenius norm minimisation method to design the gain matrix of zonotopic Kalman filter. Finally, numerical simulations of a quadruple-tank system are given to illustrate the effectiveness and superiority of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper investigates actuator fault detection and isolation for discrete-time linear descriptor systems. We propose a novel fault detection and isolation scheme based on a bank of H − / L ∞ fault detection observers (FDOs). Under the assumption that the disturbances are unknown but bounded, L ∞ analysis is used to attenuate the effects of disturbances. Meanwhile, the FDO is designed to be sensitive to the fault via considering the H − performance index in finite frequency domain. The design conditions of the proposed FDO are converted into solving a set of linear matrix inequalities. Furthermore, the residual evaluation based on a set-membership approach is proposed to reduce conservatism. Simulation results are provided to demonstrate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes a state augmentation approach to achieve interval fault estimation for descriptor systems with unknown but bounded disturbances and measurement noises. An augmented descriptor system, which is equivalent to the original system, is constructed while the fault vector is considered as an auxiliary state. Based on this augmented system, a robust fault estimation observer is designed to estimate the actuator faults. Under this augmented descriptor form, the conservatism of observer gain matrix can be significantly reduced by introducing more design degrees of freedom. Moreover, interval estimation of actuator faults can be achieved by using zonotopic techniques. Finally, a DC motor model simulation is used to illustrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Summary: This paper studies zonotopic fault detection observer design for a class of linear parameter‐varying descriptor systems. The disturbance and measurement noise are unknown but can be bounded by zonotopes. A zonotopic method is presented to estimate the envelope of residual. To attenuate the effect of disturbance and measurement noise, a zonotopic size optimization criterion is implemented based on P‐radius. To improve the fault detection performance, a finite‐frequency H_ index is introduced based on the generalized Kalman‐Yakubovich‐Popov lemma. Simulation examples of a mass‐spring and a direct‐current motor are utilized to demonstrate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

This paper considers H − ∕ H ∞ fault detection observer design in the finite frequency domain for a class of linear parameter-varying (LPV) descriptor systems. We propose a novel fault detection observer with a non-singular structure. To make the residual sensitive to faults and robust against disturbances, we develop a finite frequency H − ∕ H ∞ design method based on a generalized Kalman–Yakubovich–Popov lemma for LPV descriptor systems. Design conditions for the proposed fault detection observer are derived and converted as linear matrix inequalities. Simulation studies are conducted to demonstrate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

This paper aims to build robust feature descriptors by exploring intensity order information in a patch. To this end, the local intensity order pattern (LIOP) and the overall intensity order pattern (OIOP) are proposed to effectively encode intensity order information of each pixel in different aspects. Specifically, LIOP captures the local ordinal information by using the intensity relationships among all the neighbouring sampling points around a pixel, while OIOP exploits the coarsely quantized overall intensity order of these sampling points. These two kinds of patterns are then separately aggregated into different ordinal bins, leading to two kinds of feature descriptors. Furthermore, as these two kinds of descriptors could encode complementary ordinal information, they are combined together to obtain a discriminative and compact mixed intensity order pattern descriptor. All these descriptors are constructed on the basis of relative relationships of intensities in a rotationally invariant way, making them be inherently invariant to image rotation and any monotonic intensity changes. Experimental results on image matching and object recognition are encouraging, demonstrating the superiorities of our descriptors over the state of the art. [ABSTRACT FROM PUBLISHER]

This article proposes an actuator fault estimation approach for a class of nonlinear descriptor systems. The radial basis function (RBF) neural networks are utilised to model the actuator faults. The adaptive fault estimation observer is designed by exploiting the on-line learning ability of RBF neural networks to approximate the actuator fault. The adaptive algorithm of the RBF networks is established by the Lyapunov theory, and the design of the proposed observer is reformulated as a set of linear matrix inequalities (LMIs), which can be conveniently solved by standard LMI tools. Finally, two simulation examples are used to demonstrate the effectiveness of the proposed fault diagnosis method. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, the observer design problem for discrete-time descriptor systems is considered. For the discrete-time linear descriptor systems, the necessary and sufficient conditions for the existence and convergence of the proposed observer are given and proved, and a systemic design approach is presented via the linear matrix inequalities formulation. Furthermore, an extension to a class of nonlinear descriptor systems with Lipschitz constraints is investigated. Simulation examples are given to illustrate the estimation performance of the proposed method. [Copyright &y& Elsevier]

This paper considers the problem of set-membership estimation for discrete-time linear time-varying descriptor systems subject to unknown but bounded disturbance and noise. We propose a set-membership estimation method based on a descriptor system observer and a zonotopic estimator of the observer error bounds. The observer parameters are optimized in order to minimize the sizes of the zonotopes enclosing all admissible state trajectories. Finally, two simulation results are provided to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract In this paper, the problem of Luenberger-like interval observer design for discrete-time descriptor linear system is investigated. In the first place, two ways in which the original descriptor system is transformed into general systems are given and the detectability of the equivalent general systems is discussed in detail. A new equivalent general system construction is developed and we prove that the detectability can be kept under this new equivalent transformation. After this, based on the new equivalent general system, a Luenberger-like interval observer is proposed. Finally, two simulation examples are given to demonstrate the correctness and the effectiveness of the proposed interval observer. [ABSTRACT FROM AUTHOR]