Adaptive neuro-fuzzy inference system algorithm-based robust terminal sliding mode control MPPT for a photovoltaic system

Abstract

The non-linear power-current characteristics of a photovoltaic generator (GPV) present a challenge in maximizing power production. To address this issue, Maximum Power Point Tracking (MPPT) methods, such as the Adaptive Neuro-Fuzzy Inference System (ANFIS), are commonly used due to their quick response and minimal oscillation. As well, sliding mode control (SMC) is a popular method for controlling linear and nonlinear systems because of its high robustness. In this study, the principal purpose is to develop a new approach based on the combination of ANFIS and Terminal Robust Sliding Mode Control (ANFIS-TRSMC) to resist the PV system against uncertain conditions and track the optimal power point. The simulation results show that the ANFIS-TRSMC controller has an accurate, fast, and robust response in comparison with other algorithms such as perturb and observe and the maximum power voltage–based TRSMC controller.