Iterative Learning Control for Machining with Industrial Robots

We consider an iterative learning control (ILC) approach to machining with industrial robots. The robot and the milling process are modeled using system identification methods with a data-driven approach. Two different model-based ILC algorithms are proposed and subsequently experimentally verified in a milling scenario. The difference between the two approaches is the required sensors for acquiring relevant input data for the algorithms. The results from the experiments indicate that the proposed methods have the potential of significantly decreasing the position errors in robotic machining, up to 85% in the considered milling scenario.

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