Delay-Dependent H∞ Filtering for State-delayed LPV Systems

In this paper, we will address the design of parameter-dependent H ^infin filters for state estimation of an LPV plant with constant time-delay. It is assumed that the state-space data depend on parameters that are measurable in real-time. We will investigate the required conditions to satisfy both stability and H^infin performance in terms of linear matrix inequalities (LMIs). We take the estimation error into account as the parameter of the performance index and design the filters to minimize the Lscr₂-gain from noises to estimation error. We examine two classes of filters, one that is memoryless and another one that has memory in the filter dynamics. It is shown that the time-delayed filter results in reduced conservatism and improved performance. Illustrative example shows that the designed filters will have the ability to track the plant states in the presence of external disturbances