Gain-scheduled filtering for parameter-dependent digital systems

This paper deals with the design of gain-scheduled filters, that is filters whose state-space realization is depending on some plant parameters available in real time. Similarly to well-recognized advantages in control theory, gain-scheduled filters are expected to provide enhanced performance in comparison with customary non-adjustable filters. Our construction technique is based on a nonlinear fractional transformation representation of the system which is a generalization of widely used linear fractional transformation representations. Both generalized H₂ and H_∞ discrete-time filter design problems are investigated, together with their extension to mixed designs. This study leads to new linear matrix inequality formulations which in turn provide an effective and reliable design tool. The proposed design technique is finally evaluated in the light of simulation examples.