Abstract
Our target application is a laser additive manufacturing (LAM) process that suffers from too wide end points. To overcome this drawback, we propose to extend an algorithm of iterative learning of optimal control (ILOC) to nonlinear systems. We demonstrate its application for a LAM process, not only by simulations but also by comparing the results of 3-D metallic printing with and without learning. Extending previously published results, we provide proof of convergence of an ILOC algorithm for a wide class of nonlinear systems.
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