Regional robust H∞ filtering for dynamic systems

One of the fundamental problems in control systems is the estimation of the state variables of a dynamic system using available noisy measurements. State estimation methods in the minimum variance sense and in the min-max sense, i.e. H-infinity filtering, have been developed over the past decades, including the so-called robust filtering, namely state estimation methods which are based on an approximate model of the system and provide an optimized guaranteed performance in spite of significant parameter uncertainty in the model. Although the field of robust filtering is close to maturity, a number of important challenging problems remain unsolved. One of these problems is concerned with robust filtering for nonlinear systems. In this lecture we will discuss recent developments on regional robust H-infinity filtering for dynamic polynomial systems subject to parameter uncertainty in the system state-space model. The issues of regional H-infinity performance analysis and filter design will be analyzed.