Robust NN‐based backstepping control of non‐affine ferry ship fuel cell‐based DC MGs under stochastic disturbance inputs

Abstract

Fossil fuels play a significant role in the automobile industry, as well as marine vessel systems, with a lot of benefits like high-density and low-cost power supply, which is relatively easy to reserve, apply and carry. However, fossil fuel combustion produces several emissions such as CO2, which become greenhouse gas emissions and harmful to human health. One of the most favorable emission-free modern technologies is fuel cells, which can be applied to supply power to marine vessel propulsion systems. In such a most electrified ship, the whole of the shipboard grid can be regarded as a direct current (DC) stand-alone microgrid (MG) configuration with linear resistive and nonlinear constant power loads (CPLs). The main challenge in this new configuration of the shipboard MG is stabilizing the system's currents and voltages, subjected to stochastic disturbances arising from the effect of external winds and waves. Hence, the key objective of this research is to introduce a new modified robust adaptive stochastic backstepping controller, which is equipped with an artificial neural network, for stabilizing the current and voltage of the DC MG. The developed approach is robust against uncertainties and disturbances, has a systematic design procedure, and offers a low computational burden compared to the other complicated nonlinear controllers. In the end, a simulation is run for investigating the performance of the proposed controller over the state-of-the-art methods.