Abstract
A model based design method for the filters in a first order Iterative Learning Control (ILC) algorithm is presented and evaluated on a commercial industrial robot. Using the measurements of motor position and reference position, the error on the motor-side of the robot is minimized using ILC. The result is evaluated both on the motor-side as well as the arm-side. It is shown that the error also on the arm-side can be reduced dramatically. In the experiments it is shown that for this particular robot 5-6 iterations is enough (including a modeling experiment) to reach a steady state level of the error.
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