This paper develops an innovative robust iterative learning control law using the repetitive process setting. The new design is experimentally validated through a comprehensive set of experiments highlighting the capabilities for position tracking control of a permanent magnet synchronous motor subject to load disturbances in the presence of uncertainties in selected parameters.

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This paper develops an iterative learning control design for a class of multiple-input multipleoutput systems where a distributed heating system is used as a particular example to experimentally verify the design. The class of systems considered are 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 can result in a non-minimum phase system and hence an additional design challenge. In this work, the iterative learning control law is computed in the frequency domain by solving a convex optimization problem and its performance is evaluated in both simulation and experimentation.