ALBERT

Description: The aim of this paper is to propose a neural network based reference modified PID control which has a simple duration design method for transient characteristics improvement of digitally controlled dc-dc converters. In renewable energy network systems, various types of dc-dc converters are widely used for power conversion and such converters require a superior control method for a stable operation. Especially, transient characteristics should be improved since they heavily affect the stability of the system. For such purposes, designing of conventional control methods becomes a difficult task since the optimization of control parameters needs complicated analysis and it is affected from variations of circuit components of converters. Therefore, simple and easy design of control is widely required for a stable operation of power converters. The neural network can provide a suitable control methodology for such situation since it treats the plant as a black box and it can realize a non-linear control based on training of the input-output relation without complicated modelling and analysis. On the other hand, the neural network based method has a disadvantage caused from the fact that the neural network is trained with data obtained in advance and an overcompensation phenomenon occurs in the transient response. In this paper, the neural network control is adopted to control the dc-dc converter in coordination with a conventional PID control. The neural network predicts the output voltage of the converter and the reference value in the PID control is modified with the predictions to reduce the error of the output voltage. To avoid overcompensation, a simple duration design for the neural network control is also provided to improve the transient response effectively. From prototype testing in simulation and experiment, it is revealed that the proposed method contributes to obtain a superior transient performance compared with the conventional PID control.