Learning filter design for ILC schemes using FIR approximation over a finite frequency range

Abstract

This paper addresses the design of learning filters for a class of iterative learning control (ILC) schemes. In particular, the paper develops a method for the design of finite impulse response (FIR) filters to approximate the inverse of the dynamics resulting from the feedback controller design. The filter design is linear matrix inequality (LMI) based and guarantees convergence of the ILC scheme. Also application of the generalized Kalman-Yakubovich-Popov (KYP) lemma allows the inclusion of finite frequency range performance specifications. Finally, a simulation study illustrate the effectiveness of the new design procedure.