Applying Iterative Learning Control for Accuracy Improvement of an Electromagnetically Actuated Punch
This paper presents an application of Iterative Learning Control (ILC) for optimizing the cutting process of an electromagnetically actuated punch (EAP). In contrast to mechanical presses, with the EAP it is possible to change the ram's kinematics freely and to optimize it online. During the contact of the ram with the work piece, high transient forces are excited and deteriorate the positioning accuracy of the ram. By using a Sliding-Mode-Control it is not possible to compensate this. Thanks to the cyclic nature of the cutting process, we apply ILC in order to increase the accuracy of the ram. In this work we present a comparison study of two linear approaches. The first one consists in a filtered and phase lead compensated integral learning. In contrast, the second approach exploits explicit knowledge of the system's experimentally identified transfer function and performs a contraction mapping during the learning process. The experimental results show that both algorithms are capable to reduce the positioning error and to increase the accuracy of the system, even at high dynamics.
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