Abstract
Abstract This paper investigates tracking performances attainable in iterative learning control of linear discrete-time systems with unknown parameters in the presence of observation noise. The transient and steady-state performances are measured by the decay rate and the magnitude of tracking error, respectively. A trade-off relation between these performances is represented by the product of the direct terms of the plant and the learning law. Numerical simulations are conducted to observe the trade-off behaviors.
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