{'fraction_non_alphanumeric': 0.021141649048625793, 'fraction_numerical': 0.0, 'mean_word_length': 5.230263157894737, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A data-driven approach to power converter control via convex optimization

A new model-reference data-driven approach is presented for synthesizing controllers for the CERN power converter control system. This method uses the frequency response function (FRF) of a system in order to avoid the problem of unmodeled dynamics associated with low-order parametric models. For this particular application, it is shown that a convex optimization problem can be formulated in the H∞ sense to shape the closed-loop FRF while guaranteeing the closed-loop stability. This optimization problem is realized by linearizing a non-convex constraint around a stabilizing operating point. The effectiveness of the method is illustrated by designing a controller for the SATURN power converter which is used in the Large Hadron Collider, in injector machines, and for pulsed applications at CERN. Experimental validation in the frequency-domain is also presented.