{'fraction_non_alphanumeric': 0.028846153846153848, 'fraction_numerical': 0.0, 'mean_word_length': 5.3106060606060606, '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}}
Feedback-assisted iterative learning control based on a nonlinear fuzzy model

As a feedforward control strategy, iterative learning control (ILC) is used to track a pre-defined reference and reject repetitive disturbances iteratively, but it is incapable of compensating for non-repetitive disturbances. Thus, ILC is often combined with a well-designed feedback controller. Considering nonlinear process, this paper presents an integrated ILC and on-line model predictive (MPC) strategy for wide range-operation, which base on a fuzzy model. In the overall control law, the feedforward ILC contributes the majority of the control signal, while the feedback MPC is meant to be supplementary to regulate the control signal and reject disturbances. The performance of the feedback-assisted ILC is illustrated by a steam-boiler system.