Robust State and fault Estimation of Linear Discrete Time Systems with Unknown Disturbances

This paper presents a new robust fault and state estimation based on recursive least square filter for linear stochastic systems with unknown disturbances. The novel elements of the algorithm are : a simple, easily implementable, square root method which is shown to solve the numerical problems affecting the unknown input filter algorithm and related information filter and smoothing algorithms; an iterative framework, where information and covariance filters and smoothing are sequentially run in order to estimate the state and fault. This method provides a direct estimate of the state and fault in a single block with a simple formulation. A numerical example is given in order to illustrate the performance of the proposed filter. Keywords—Kalman filtering, unbiased minimum-variance, state and fault estimation, unknown disturbance, square root, linear discrettime systems.