An Adaptive Integral Backstepping SMC and Robust Functional Expanded Multikernel BLS Based MPPT Control in PV-Battery DC Microgrid System

In this paper, a robust functional expanded multikernel broad learning system (RFEMBLS) is proposed to compute the complex nonlinear solar photovoltaic (PV) reference voltage more accurately by importing the irradiance and temperature in different uncertainty conditions. The novel droop control mechanism is introduced to obtain reference current to reduce dependence on different connected renewable energy resources. An adaptive integral backstepping sliding mode controller (AIBSMC) is designed to control the DC bus voltage under different abnormal scenarios for the proposed DC microgrid. An asymptotical stability analysis is developed using Lyapunov theory for the PV-battery DC microgrid. A new adoption rule is proposed for the estimation of both references of PV voltage and battery current. Furthermore, the system steady-state error and tracking convergence of the error are improved by adding an integral action. The backstepping method based on the DC bus voltage feedback results in a faster response and negligible chattering. A sliding mode controller is proposed to improve the control precision and robustness. Finally, the proposed RFEMBLS-AIBSMC method is tested using dSPACE platform in the scale-down lab environment to verify the robustness, practicability, feasibility, and efficacy of the proposed system in the real-time scenario.