Game-Theoretic Design for Robust $H_{infty}$ Filtering and Deconvolution With Consideration of Known Input

摘要

This paper aims at the $H_{infty}$ estimation of a linear combination of state and unknown input for linear continuous time-varying systems, which is subjected to both norm-bounded parameter uncertainty and a known input. Such a problem is reformulated into a two-player differential game, whose saddle point solution gives rise to both one sufficient solvable condition to the estimation problem and one possible optimal estimator in terms of solution to two coupled Riccati differential equations. It is demonstrated, through one example, the proposed estimator is of superior robust performance with respect to either parameter uncertainty or known input, when compared with those based on the nominal design.