Robust Filtering for State and Fault Estimation of Linear Stochastic Systems with Unknown Disturbance

Hmida, Fayçal Ben; Khemiri, Karim; Ragot, José; Gossa, Moncef

doi:10.1155/2010/591639

Cited by 12 publications

(13 citation statements)

References 20 publications

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“…Firstly, a three-stage U − V transformations are made in order to decouple the covariance matrix on the augmented state Kalman Filter (ASKF) so, an optimal structure named optimal three-stage Kalman filter (OThSKF)is obtained. Secondly, an unbiased fault and state estimation are elaborated by modifying in measurement update equations of the (OThSKF), when the fault and the unknown disturbances affect the state and the measurement equations, we propose an augmented robust three-stage Kalman filter (ARThSKF) [4].…”

Section: Fault Detectionmentioning

confidence: 99%

“…An optimal observer is proposed to estimate the state which is designed to be decoupled from unknown disturbances with minimum variance for time varying systems with both noise and unknown disturbances. In fact Ben Hmida in [4], has developed a robust filter structure, that can solve the problem of simultaneously estimating the state and the fault in the presence of the unknown disturbances. The method is based on the assumption that no prior knowledge about the dynamical evolution of the fault and the availability of unknown input.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fault detection performances analysis for stochastic systems based on adaptive threshold

Houiji

Hamdaoui

Aoun

2016

2016 13th International Multi-Conference on Systems, Signals &Amp; Devices (SSD)

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This paper investigates the problem of fault detection for discrete linear systems subjected to unknown disturbances, actuator and sensor faults. A bank of Augmented Robust Three stage Kalman filters is adapted to estimate both the state and the fault as well as to generate the residuals. Besides, this paper presents the evaluation of the residuals with Bayes test of binary hypothesis test for fault detection to adaptive threshold compared with fixed threshold. This test allow the detection of low magnitude faults as fast as possible with a minimum risk of errors, the increase of detection probability and the reduction of false alarm probability.

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Section: Fault Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fault detection performances analysis for stochastic systems based on adaptive threshold

Houiji

Hamdaoui

Aoun

2016

2016 13th International Multi-Conference on Systems, Signals &Amp; Devices (SSD)

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“…T is an arbitrary nonsingular transformation matrix of appropriate dimension. The filter (12) can then be equivalently rewritten The unique solution to (18.a) coincides with the Kalman filter's gain…”

Section: Kalman Filter For Stochastic Linear System Subject To Imentioning

confidence: 99%

Kalman Filter for Discrete-Time Stochastic Linear Systems Subject to Intermittent Unknown Inputs

Keller

Sauter

2013

IEEE Trans. Automat. Contr.

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State estimation of stochastic discrete-time linear systems subject to persistent unknown inputs has been widely studied but only few works have been dedicated to the case where unknown inputs may be simultaneously or sequentially active or inactive. In this paper, a Kalman filter approach is proposed for state estimation of systems with unknown intermittent inputs. The design is based on the minimisation of the trace of the state estimation error covariance matrix under the constraint that the state estimation error is decoupled from the unknown inputs corrupting the system at the current time. The necessary and sufficient stability conditions are established considering the upper bound of the prediction error covariance matrix.Index Terms-Kalman filter, intermittent unknown inputs, linear system, covariance matrices.

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“…Robust two-stage Kalman filter [19], which is equivalent to Kitanidis's filter, has developed a robust filter that uses the two-stage Kalman filtering technique and unknown inputs filtering technique. Hmida et al [20,21] have proposed the robust three-stage Kalman filter (RThSKF) for state and fault estimation with unknown inputs of linear systems.…”

Section: Introductionmentioning

confidence: 99%

Extension of robust three‐stage Kalman filter for state estimation during Mars entry

Xiao

et al. 2014

IET Radar, Sonar & Navigation

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In the context of unknown inputs (unknown dynamic biases) and unknown measurement systematic errors, this article studies the extension of robust three-stage Kalman filter (ERThSKF) that satisfies high precision entry navigation filter requirements for Mars pinpoint landing mission. The imprecise dynamic model with uncertain parameters could produce unknown inputs. Using radiometric beacons and/or inertial measurement unit (IMU) as output observation during the Mars atmospheric entry phase, the measurement data from the radio range and/or IMU have unknown measurement systematic errors. To solve these problems, we made an ERThSKF to obtain the accurate state estimation of the uncertain non-linear Mars system with unknown inputs and unknown measurement systematic errors which can be effectively estimated and compensated. Computer simulations show that the proposed navigation filter algorithm in this study can achieve high convergence speed and minor errors, which fulfils the need of future pinpoint Mars landing missions.

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Robust Filtering for State and Fault Estimation of Linear Stochastic Systems with Unknown Disturbance

Cited by 12 publications

References 20 publications

Fault detection performances analysis for stochastic systems based on adaptive threshold

Fault detection performances analysis for stochastic systems based on adaptive threshold

Kalman Filter for Discrete-Time Stochastic Linear Systems Subject to Intermittent Unknown Inputs

Extension of robust three‐stage Kalman filter for state estimation during Mars entry

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