Model Reduction for Linear Parameter-Dependent Systems

Abstract

The paper considers the problem of model reduction for a class of linear parameter-dependent (LPD) systems. Three model reduction approaches: balanced truncation, balanced LQG truncation and gain-scheduled $mathcal{H}_infty$ model reduction, are presented to reduce the dimension of LPD systems. For the former two approaches, conditions to proceed the reduction are given in terms of a finite number of linear matrix inequalities (LMIs); while the latter one involves LMIs with some additional rank constraint