Research Repository

Control of a Buck DC/DC Converter Using Approximate Dynamic Programming and Artificial Neural Networks

Dong, Weizhen and Li, Shuhui and Fu, Xingang and Li, Zhongwen and Fairbank, Michael and Gao, Yixiang (2021) 'Control of a Buck DC/DC Converter Using Approximate Dynamic Programming and Artificial Neural Networks.' IEEE Transactions on Circuits and Systems Part 1: Regular Papers, 68 (4). pp. 1760-1768. ISSN 1549-8328

ANN_dc_dc_Buck_010521_final_submitted.pdf - Accepted Version

Download (1MB) | Preview


This paper proposes a novel artificial neural network (ANN) based control method for a dc/dc buck converter. The ANN is trained to implement optimal control based on approximate dynamic programming (ADP). Special characteristics of the proposed ANN control include: 1) The inputs to the ANN contain error signals and integrals of the error signals, enabling the ANN to have PI control ability; 2) The ANN receives voltage feedback signals from the dc/dc converter, making the combined system equivalent to a recurrent neural network; 3) The ANN is trained to minimize a cost function over a long time horizon, making the ANN have a stronger predictive control ability than a conventional predictive controller; 4) The ANN is trained offline, preventing the instability of the network caused by weight adjustments of an on-line training algorithm. The ANN performance is evaluated through simulation and hardware experiments and compared with conventional control methods, which shows that the ANN controller has a strong ability to track rapidly changing reference commands, maintain stable output voltage for a variable load, and manage maximum duty-ratio and current constraints properly.

Item Type: Article
Uncontrolled Keywords: dc/dc buck converter; artificial neural network; approximate dynamic programming; optimal control
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 08 Feb 2021 14:33
Last Modified: 15 Jan 2022 01:36

Actions (login required)

View Item View Item