Optimal residual generation for fault detection in linear discrete time-varying systems with uncertain observations

Li, Yueyang; Karimi, Hamid Reza; Ahn, Choon Ki; Xu, Yuan; Zhao, Dong

doi:10.1016/j.jfranklin.2018.02.012

Cited by 25 publications

(14 citation statements)

References 45 publications

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“…Observing the fact that the converted system includes uncertain matrices DH k and D B u, k in equation (6), to find a suitable f kjk that satisfies equation (8) or the original performance index (2), we construct an equivalent form of equation 5as follows…”

Section: Problem Formulationmentioning

confidence: 99%

Fault estimation for a class of nonlinear time-variant systems through a Krein space–based approach

Zhang

et al. 2020

Measurement and Control

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This paper studies the [Formula: see text] fault estimation problem for a class of discrete-time nonlinear systems subject to time-variant coefficient matrices, online available input, and exogenous disturbances. By assuming that the concerned nonlinearity is continuously differentiable and by using Taylor series expansions, the dynamic system is transferred as a linear time-variant system with modeling uncertainties. A non-conservative but nominal system and its corresponding [Formula: see text] indefinite quadratic performance function are, respectively, given in place of the transferred uncertain system and the conventional performance metric, such that the estimation problem is converted as a two-stage optimization issue. By introducing an auxiliary model in Krein space, the so-called orthogonal projection technique is utilized to search an appropriate choice serving as the estimation of the fault signal. A necessary and sufficient condition on the existence of the fault estimator is given, and a recursive algorithm for computing the gain matrix of the estimator is proposed. The addressed method is applied to an indoor robot localization system to show its effectiveness.

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Section: Problem Formulationmentioning

confidence: 99%

Fault estimation for a class of nonlinear time-variant systems through a Krein space–based approach

Zhang

et al. 2020

Measurement and Control

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“…As one of the most used data fusion algorithm, the Kalman filter (KF) and its improving methods have been widely used in many fields [30][31][32][33][34]. And its pseudo code is listed as Algorithm 1.…”

Section: Kf Algorithmmentioning

confidence: 99%

Robust Self-Contained Pedestrian Navigation by Fusing the IMU and Compass Measurements via UFIR Filtering

Hou

Liu

2018

Journal of Electrical and Computer Engineering

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In order to overcome the poor observability of yaw measurement for foot-mounted inertial measurement unit (IMU), an integrated IMU+Compass scheme for self-contained pedestrian navigation is presented. In this mode, the compass measurement is used to provide the accurate yaw to improve the accuracy of the attitude transformation matrix for the foot-mounted IMU solution. And then, when the person is in a stance phase during walk, a unbiased finite impulse response (UFIR) filter based on the self-contained pedestrian navigation scheme is investigated, which just needs the state vector size 푀 and the filtering horizon size 푁 , while ignoring the noise statistics compared with the Kalman filter (KF). Finally, a real test has been done to verify the performance of the proposed self-contained pedestrian navigation using the IMU and compass measurements via UFIR filter. The test results show that the proposed filter has robust performance compared with the KF.

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“…Among the different approaches to design FDI systems, the FDI based on state observers has proved to be one of the best trade-offs between performance and applicability [3]. A wide range of FDI methods based on observers can be found in the literature, for instance, by considering a Kalman filter [4],  ∞ filter [5][6][7], FDI and fault tolerant control with time-delay [5,8], mixed  ∞ ∕ − [9,10] Guillermo Valencia-Palomo is with Tecnológico Nacional de México / Instituto Tecnolgico de Hermosillo, Ave. Tecnológico y Periférico Poniente S/N C.P. 83170, Hermosillo, Mexico.…”

Section: Introductionmentioning

confidence: 99%

Sensor Fault Diagnosis Based on a Sliding Mode and Unknown Input Observer for Takagi‐Sugeno Systems with Uncertain Premise Variables

Gómez-Peñate

Valencia‐Palomo

López‐Estrada

et al. 2018

Asian Journal of Control

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This paper presents the design of a  ∞ sliding mode and an unknown input observer for Takagi-Sugeno (TS) systems. Contrary to the common approaches reported in the literature, which considers exact premise variables, this work deals with the problem of inexact measurements of the premise variables. The proposed method is based on a  ∞ criteria to be robust to disturbances, sensor noise and uncertainty on the premise variables. The observer convergence and stability are established by considering a quadratic Lyapunov function, which relies on a set of Linear Matrix Inequalities. Then, a dedicated observer scheme is considered to detect and isolate sensor faults. Finally, the performance and applicability of the proposed approach are illustrated through numerical experiments on a nonlinear model that represents the lateral dynamics of an electric vehicle.

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Optimal residual generation for fault detection in linear discrete time-varying systems with uncertain observations

Cited by 25 publications

References 45 publications

Fault estimation for a class of nonlinear time-variant systems through a Krein space–based approach

Fault estimation for a class of nonlinear time-variant systems through a Krein space–based approach

Robust Self-Contained Pedestrian Navigation by Fusing the IMU and Compass Measurements via UFIR Filtering

Sensor Fault Diagnosis Based on a Sliding Mode and Unknown Input Observer for Takagi‐Sugeno Systems with Uncertain Premise Variables

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