Data-Mining-Based Hardware-Efficient Neural Network Controller for DC–DC Switching Converters

Abstract

For the exiting neural network (NN)-based controllers, since the training data are not optimized using different control algorithms, their controlling performance improvements are limited. This paper proposes a data-mining-based hardware-efficient NN controller for DC–DC switching converters. Firstly, the data mining strategy for NN controllers is proposed to optimize the training data. In data mining process, the optimal control data are selected from different control algorithms to create an optimized dataset for training NN controller. In addition, to reduce the hardware resources, the structure of a hardware-efficient NN controller is proposed, which comprises coarse-tuning NN (NN-ct) and fine-tuning NN (NN-ft) controllers, and the switching between two NN controllers is conducted based on the output voltage variation. The NN-ct and NN-ft controllers are trained using the optimized fine-tuning and coarse-tuning sub-datasets, respectively. By switching the weights and biases and disabling some hidden-layer nodes, two separate NN controllers can share some hardware resources, thus reducing the hardware cost without sacrificing the performance. The simulation and experimental results demonstrate that data-mining-based NN controller has better performance than other NN controllers. Furthermore, the hardware resource requirement of a hardware-efficient NN controller can be reduced by at least 17% when compared with an ordinary NN controller.