Iterative learning control for a class of multivariable distributed systems with experimental validation

Abstract

This article develops an iterative learning control (ILC) design for a class of multiple-input–multiple-output systems where a distributed heating system is used as a particular example to experimentally validate the design. The class of systems considered is described by a parabolic partial differential equation, which, for control design, is approximated by a finite dimensional state-space model obtained by applying the method of integro-differential relations combined with a projection approach. In some cases, including the distributed heating system, this approximation may result in a nonminimum phase system and, hence, pose an additional design challenge. In this work, the ILC law is computed in the frequency domain by solving a convex optimization problem, and its performance is evaluated in both simulation and experiments.

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ORCID for Eric Rogers: orcid.org/0000-0003-0179-9398

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