Iteration varying filters in Iterative Learning Control, Report no. 2435

In this contribution it is shown how an iterative learning control algorithm can be found for a disturbance rejection application where a repetitive disturbance is acting on the output of a system. It is also assumed that there is additive noise on the measurements from the system. When applying iterative learning control to a system where measurement disturbances are present it is shown that it is optimal to use iteration varying filters in the learning law. To achieve a good transient behavior it is also necessary to have an accurate model of the system. The results are also verified in simulations.