Renewable energy sources are becoming increasingly critical for combating global electricity shortages and mitigating environmental pollution. Among these, solar energy stands out for it s abundance, minimal environmental impact, and widespread availability across diverse regions. However, optimizing solar energy production is essential to maximize its potential by reducing costs, improving efficiency, and fully utilizing this ever -present resource. This research introduces two novel, AI -powered nonlinear control methodologies designed for precise Maximum Power Point Tracking (MPPT) in photovoltaic (PV) systems. These methodologies are specifically crafted to handle dynamic changes in solar irradiance and temperature with exceptional adaptability. The proposed techniques' performance is rigorously evaluated through simulations conducted within the MATLAB/Simulink environment. The evaluation focuses on their effectiveness under various system conditions, employing two distinct DC -DC converter configurations for a comprehensive analysis.

The power-current relationship of a photovoltaic generator (GPV) is non-linear and contingent upon environmental factors. Nonetheless, achieving the highest possible power output from a GPV can only occur at a specific point along the characteristic curve. The development of Maximum Power Point Tracking (MPPT) techniques is fundamental to designing solar systems that optimize power generation. The Adaptive Fuzzy Neural Inference System (ANFIS) is one of the most effective ways to attain the maximum power point (MPP) in PV systems due to its prompt response time and minimal oscillations. Furthermore, sliding mode control (SMC) is a popular method for managing linear and nonlinear systems because of its robustness. The primary objective of this research is to introduce a novel approach that utilizes a combination of ANFIS and Sliding Mode Control (ANFIS-SMC) to safeguard the PV system against uncertain conditions and achieve the optimum power point. The simulation outcomes indicate that the ANFIS-SMC controller delivers a precise, swift, and resilient response, compared to other algorithms like perturb and observe (P&O).

In this paper, we present a comparison of three types of algorithms: Perturb and Observe (P&O), Incremental Conductance (InCnd), and Fuzzy Logic Controller (FLC) that track the maximum power point (MPP) of a photovoltaic system (PV) over varying conditions of solar irradiation and temperature. The PV system is composed of solar panels, a resistive load, and an MPPT controller with pulse width modulation (PWM) technique for driving the DC-DC Quadratic Boost converter (QBC). This comparison, based on three criteria, namely stability, time response and, power efficiency, demonstrates the benefit of employing an MPPT with variable step monitoring. The energy obtained using those three algorithms is practically similar, with a considerable improvement for the Fuzzy Logic Controller.