Transient Response of Reference Modified Digital PID Control DC-DC Converters with Neural Network Prediction


IEICE TRANSACTIONS on Communications   Vol.E99-B   No.11   pp.2340-2350
Publication Date: 2016/11/01
Publicized: 2016/06/17
Online ISSN: 1745-1345
DOI: 10.1587/transcom.2016EBP3095
Type of Manuscript: PAPER
Category: Energy in Electronics Communications
dc-dc converter,  neural network,  prediction,  digital control,  

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This paper presents a novel control method based on predictions of a neural network in coordination with a conventional PID control to improve transient characteristics of digitally controlled switching dc-dc converters. Power supplies in communication systems require to achieve a superior operation for electronic equipment installed to them. Especially, it is important to improve transient characteristics in any required conditions since they affect to the operation of power supplies. Therefore, dc-dc converters in power supplies need a superior control method which can suppress transient undershoot and overshoot of output voltage. In the presented method, the neural network is trained to predict the output voltage and is adopted to modify the reference value in the PID control to reduce the difference between the output voltage and its desired one in the transient state. The transient characteristics are gradually improved as the training procedure of the neural network is proceeded repetitively. Furthermore, the timing and duration of neural network control are also investigated and devised since the time delay, which is one of the main issue in digital control methods, affects to the improvement of transient characteristics. The repetitive training and duration adjustment of the neural network are performed simultaneously to obtain more improvement of the transient characteristics. From simulated and experimental results, it is confirmed that the presented method realizes superior transient characteristics compared to the conventional PID control.