Maximum Power Extraction from a Standalone Photo Voltaic System via Neuro-Adaptive Arbitrary Order Sliding Mode Control Strategy with High Gain Differentiation

In this work, a photovoltaic (PV) system integrated with non-inverting DC-DC buck-boost converter to extract maximum power under varying environmental conditions such as irradiance and temperature is considered. order (via transfer theorem), robust nonlinear arbitrary sliding mode-based control designed for tracking the desired reference, which generated via feed forward neural networks (FFNN). The proposed law utilizes some states of system, are estimated use high gain differentiator famous flatness property systems. This synthetic strategy named neuro-adaptive mode (NAAOSMC). overall closed-loop stability discussed in detail simulations carried out Simulink environment MATLAB endorse effectiveness developed strategy. Finally, comparison controller backstepping done, ensures performance terms extraction, steady-state error more robustness against sudden variations atmospheric conditions.