Artificial Neural Network-Based Control Strategy Implemented on a Multiport DC-DC Converter in a DC Microgrid

Abstract

This paper proposes an artificial neural network (ANN)-based control strategy for the bidirectional dual-input single-output (BDISO) DC-DC converter. The approach aims to combine the robustness of nonlinear control with the computational efficiency of data-driven implementation. A supertwisting sliding mode controller (STSMC) is first employed offline to generate representative state-action datasets that capture the converter's nonlinear dynamics under both buck and boost modes. The extracted state features and corresponding optimal switching actions are then used to train an ANN, enabling it to approximate the STSMC control law. During online operation, the trained ANN directly produces low-latency switching commands for voltage regulation, thereby eliminating the need for real-time STSMC computation. The proposed controller is evaluated in MATLAB/Simulink and benchmarked against conventional proportional-integral (PI) and fixed-frequency sliding mode control (FSMC) methods. Simulation results demonstrate that the ANN-based controller achieves superior transient response, precise voltage regulation, and reduced chattering, confirming its potential as a compact and efficient solution for multi-port DC-DC converter applications.