{'fraction_non_alphanumeric': 0.018163471241170535, 'fraction_numerical': 0.004036326942482341, 'mean_word_length': 5.161490683229814, '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}}
Reference modification control DC-DC converter with neural network predictor

The purpose of this paper is to present a new digital control method for dc-dc converters by reference modification with the neural network predictor. In the proposed method, the reference in the proportional control term of the conventional PID control is modified using the neural network predictor during the transient interval. The neural network is repeatedly trained to predict the output voltage using former predicted data for the modification of the reference. After the training, the reference in the P control is modified by the predictor to improve the transient response. By using the proposed method, the undershoot of output voltage is suppressed to 41% compared with the conventional method's one. The convergence time is also improved to 48% compared with the conventional method's one. Therefore, it is confirmed that the proposed method has the superior performance to control dc-dc converters.