ANN-Barrier Function-Based Adaptive Robust Nonlinear Control of DC Microgrid

Abstract

Recently, DC microgrids (DC-MGs) have gained an immense popularity due to their unique characteristics such as structural simplicity and implementation, high efficiency, high power quality, and their capability of incorporating hybrid energy storage system (HESS). But nonlinear nature of power conversion units and effect of external disturbances pose a challenge in designing an effective control strategy for the control of DC-MG. Therefore, this study proposes an adaptive barrier function-based robust nonlinear controller for regulating the DC bus voltage by tracking the desired currents of the power sources in the presence of time-varying disturbances of unknown upper bound. Furthermore, ANN-based reference generation scheme is employed for acquiring the maximum power from PV system under changing irradiance and temperature. The designed controller reduces the overestimation of adaptive gains which was the problem of conventional adaptive sliding mode-based controllers (ASMC). Lyapunov stability criterion is used to analyze the stability of the system. The proposed controller is simulated in MATLAB/Simulink and has been compared with ASMC. The results show better dynamic performance of the system under disturbances/uncertainties. The controller has also been experimentally validated using MS320F28379D Dual-Core microcontroller in hardware in loop-based setup.