Control of a Buck DC/DC Converter Using Approximate Dynamic Programming and Artificial Neural Networks
Weizhen Dong, Shuhui Li, Xingang Fu, Zhongwen Li, Michael Fairbank, Yixiang Gao
Abstract
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.