A STABLE RECURSIVE FILTER FOR STATE ESTIMATION OF LINEAR MODELS IN THE PRESENCE OF BOUNDED DISTURBANCES

This contribution proposes a robust recursive algorithm for state estimation of linear multi-output systems with unknown but bounded disturbances corrupting both the state and measurement vectors. A novel approach based on state bounding techniques is presented. The proposed algorithm can be decomposed into two steps : time updating and observation updating that uses a switching estimation Kalman-like gain matrix. Particular emphasis will be given to the design of a weighting factor that ensures consistency of the estimated state vectors with the input-output data and the noise constraints and that enforces convergence properties.