Robust fault and state estimation for linear discrete-time systems with unknown disturbances using PI Three-Stage Kalman Filter

Khemiri, Karim; Gannouni, F.; Hmida, Fayçal Ben; Gossa, Moncef; Ragot, José

doi:10.1109/ccca.2011.6031199

Cited by 3 publications

(2 citation statements)

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“…Later, in [24] the problem of joint fault and state estimation of linear systems in the presence of unknown input with uncertain noise covariances was presented. This problem was solved by using the proportional integral three-stage Kalman filter (PI-ThSKF) to estimate the state and the fault of stochastic discrete-time systems with unknown inputs.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

See 1 more Smart Citation

Simultaneous Robust Fault and State Estimation for Linear Discrete‐Time Uncertain Systems

Gannouni

Hmida

2017

Mathematical Problems in Engineering

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We consider the problem of robust simultaneous fault and state estimation for linear uncertain discrete-time systems with unknown faults which affect both the state and the observation matrices. Using transformation of the original system, a new robust proportional integral filter (RPIF) having an error variance with an optimized guaranteed upper bound for any allowed uncertainty is proposed to improve robust estimation of unknown time-varying faults and to improve robustness against uncertainties. In this study, the minimization problem of the upper bound of the estimation error variance is formulated as a convex optimization problem subject to linear matrix inequalities (LMI) for all admissible uncertainties. The proportional and the integral gains are optimally chosen by solving the convex optimization problem. Simulation results are given in order to illustrate the performance of the proposed filter, in particular to solve the problem of joint fault and state estimation.

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Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

“…When the condition fails at any particular iteration, the desired performance is lost and the filter can diverge. In addition the disadvantages of the existing approaches [3,4,24,25] are that the filter lost its optimality in the presence of uncertainties in the state and the output matrices.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%