{'fraction_non_alphanumeric': 0.0073964497041420114, 'fraction_numerical': 0.0, 'mean_word_length': 5.838383838383838, '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}}
Experimental implementation of iterative learning control for processes with stochastic disturbances

A number of iterative learning control algorithms have been developed in a stochastic setting in recent years. The results currently available are in the form of fundamental systems theoretical properties and associated algorithm development. This paper reports results from the application of a stochastic algorithm on a gantry robot system that has been used in the benchmarking a range of deterministic algorithms. These results confirm that this algorithm is capable of delivering good performance in the experimental domain, including comparison against an alternative.