Karim Khemiri

Three-stage Kalman filter for state and fault estimation of linear stochastic systems with unknown inputs

Abstract The paper studies the problem of simultaneously estimating the state and the fault of linear stochastic discrete-time varying systems with unknown inputs. The fault and the unknown inputs affect both the state and the output. However, if the dynamical evolution models of the fault and the unknown inputs are available the filtering problem will be solved by the Optimal three-stage Kalman Filter (OThSKF). The OThSKF is obtained after decoupling the covariance matrices of the Augmented state Kalman Filter (ASKF) using a three-stage U–V transformation. Nevertheless, if the fault and the unknown inputs models are not perfectly known the Robust three-stage Kalman Filter (RThSKF) will be applied to give an unbiased minimum-variance estimation. Finally, a numerical example is given in order to illustrate the proposed filters.

Novel optimal recursive filter for state and fault estimation of linear stochastic systems with unknown disturbances

Novel optimal recursive filter for state and fault estimation of linear stochastic systems with unknown disturbances This paper studies recursive optimal filtering as well as robust fault and state estimation for linear stochastic systems with unknown disturbances. It proposes a new recursive optimal filter structure with transformation of the original system. This transformation is based on the singular value decomposition of the direct feedthrough matrix distribution of the fault which is assumed to be of arbitrary rank. The resulting filter is optimal in the sense of the unbiased minimum-variance criteria. Two numerical examples are given in order to illustrate the proposed method, in particular to solve the estimation of the simultaneous actuator and sensor fault problem and to make a comparison with the existing literature results.

Unbiased Minimum-Variance Filter for State and Fault Estimation of Linear Time-Varying Systems with Unknown Disturbances

This paper presents a new recursive filter to joint fault and state estimation of a linear time-varying discrete systems in the presence of unknown disturbances. The method is based on the assumption that no prior knowledge about the dynamical evolution of the fault and the disturbance is available. As the fault affects both the state and the output, but the disturbance affects only the state system. Initially, we study the particular case when the direct feedthrough matrix of the fault has full rank. In the second case, we propose an extension of the previous case by considering the direct feedthrough matrix of the fault with an arbitrary rank. The resulting filter is optimal in the sense of the unbiased minimum-variance (UMV) criteria. A numerical example is given in order to illustrate the proposed method.

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

This paper presents a new robust filter structure to solve the simultaneous state and fault estimation problem of linear stochastic discrete-time systems with unknown disturbance. The method is based on the assumption that the fault and the unknown disturbance affect both the system state and the output, and no prior knowledge about their dynamical evolution is available. By making use of an optimal three-stage Kalman filtering method, an augmented fault and unknown disturbance models, an augmented robust three-stage Kalman filter (ARThSKF) is developed. The unbiasedness conditions and minimum-variance property of the proposed filter are provided. An illustrative example is given to apply this filter and to compare it with the existing literature results.

Recursive least-squares estimation for the joint input-state estimation of linear discrete time systems with unknown input

This paper presents a recursive least-squares approach to estimate simultaneously the state and the unknown input of linear time varying discrete time systems with unknown input. The method is based on the assumption that no prior knowledge about the dynamical evolution of the input is available. The joint input and state estimation are obtained by recursive least-squares formulation by applying the inversion lemmas. The proposed filter is equivalent to recursive three step filter. To illustrate the performance of the proposed filter an example is given.

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

The problem of simultaneously estimating the state and the fault of linear time varying stochastic systems in the presence of unknown input with uncertain noise covariances is presented. The approach suggested rests on the use of the Proportional Integral Three-Stage Kalman Filter (PI-ThSKF). This technique is qualified to be robust against the noise covariance matrices uncertainty. The proposed filter is tested by an illustrative example.