On iterative learning from different tracking tasks in the presence of time-varying uncertainties
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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For the control of cyclic processes Iterative Learning Control (ILC) has been proven to be an efficient concept. In this contribution a parametric framework is proposed that uses an analogy to a discrete time control loop and intentionally avoids highly theoretical concepts such as two dimensional systems theory. The generality with respect to the structure of the learning law, to different parametrisations and to different classes of systems is demonstrated. A brief analysis is followed by the detailed discussion of learning laws in the time and frequency domain. The latter is illustrated both in a simulation study and in an experiment on a hydraulic test bench for materials testing.