Robust Kalman Filtering for Uncertain Systems Subject to Unknown Inputs: The Discrete-Time Case

Hayar, Mohamed; Zasadzinski, Michel; Darouach, Mohamed

doi:10.1016/s1474-6670(17)58387-1

Cited by 4 publications

(2 citation statements)

References 10 publications

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“…To univocally isolate a fault concerning one of the input sensors, under the assumption that output sensors are fault-free, a bank of Kalman filters with unknown inputs is used 10 . The number of these filters is equal to the number r of control inputs.…”

Section: C P(t + L\t) Is the Covariance Matrix Of The One Step Predimentioning

confidence: 99%

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Kalman Filtering to Enhance the Gas Turbine Control Sensor Fault Detection

Simani¹,

Spina²

1999

Theory and Practice of Control and Systems

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In this paper an application of a procedure for the detection and isolation of abrupt changes (such as faults) in input-output control sensors of a single shaft industrial gas turbine is presented. The system considered is modeled as a linear dynamic system corrupted by stochastic additive noise. The diagnosis system involves the design of Kalman filters with unknown inputs and uses statistical tests on filter innovations. The results are compared with the ones suggested in a related work and obtained by using model-based observers and a geometrical analysis of residuals.

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Section: C P(t + L\t) Is the Covariance Matrix Of The One Step Predimentioning

confidence: 99%

“…The design of the Kalman filter with unknown in put was obtained by using a straightforward procedure, simpler than the one given by Hayar et al 10 . (xi\ (t)) and of the fuel mass flow rate Mf (u 2 (t)).…”

Section: It Ensues That Ifmentioning

confidence: 99%

Kalman Filtering to Enhance the Gas Turbine Control Sensor Fault Detection

Simani¹,

Spina²

1999

Theory and Practice of Control and Systems

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Conclusions

Puig

Simani

2021

Diagnosis and Fault‐tolerant Control 2

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Discrete-time singular observers: H/sub 2//H/sub ∞/ optimality and unknown inputs

Takahashi

Palhares²,

Peres³

Proceedings of the 36th IEEE Conference on Decision and Control

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This note presents a family of observers which has as particular cases the "unknown input observers" and the "linear quadratic/Xz /Hoe optimal observers". A smooth transition between these particular extreme cases through the general case is provided, corresponding to a transition of the disturbances patterns from entirely "singular" to entirely "regular" ones. Both "hard" (arbitrary) and "soft" (Gaussian or signals) disturbances are allowed.

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Robust Kalman Filtering for Uncertain Systems Subject to Unknown Inputs: The Discrete-Time Case

Cited by 4 publications

References 10 publications

Kalman Filtering to Enhance the Gas Turbine Control Sensor Fault Detection

Kalman Filtering to Enhance the Gas Turbine Control Sensor Fault Detection

Conclusions

Discrete-time singular observers: H/sub 2//H/sub ∞/ optimality and unknown inputs

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