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

Abstract EN

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.