A Recursive Least Squares-Based Adaptive Fuzzy Logic Approach for High-Performance Maximum Power Point Tracking in Photovoltaic Systems

Abstract

The growing demand for high-efficiency renewable energy systems has intensified research into advanced photovoltaic (PV) energy optimization techniques. Maximum Power Point Tracking (MPPT) is essential for maximizing energy extraction from PV systems under varying environmental conditions. Among the available techniques, intelligent control strategies such as Fuzzy Logic Control (FLC) and Adaptive Fuzzy Logic Control (AFLC) have attracted considerable attention due to their robustness and adaptability. However, conventional methods, including standard FLC, often suffer from reduced tracking accuracy, slower dynamic response, and increased steady-state oscillations under rapidly changing irradiance and temperature conditions—challenges that are particularly pronounced in tropical environments with frequent atmospheric fluctuations. This study presents the design and simulation of an Adaptive Fuzzy Logic Controller (AFLC) based MPPT system for a PV array integrated with a DC–DC boost converter. The system is modeled in MATLAB/Simulink, where both FLC and AFLC techniques are implemented and evaluated under varying environmental conditions. The proposed AFLC enhances performance by dynamically adjusting its membership functions, scaling factors, and rule weights in real time using Recursive Least Squares (RLS) adaptation. It was observed that Simulation results of the AFLC significantly outperforms the conventional FLC approach. The AFLC achieves a tracking efficiency of 91.36%, representing a 5.78% improvement over FLC. The response time is reduced by 90.9% (from 0.11 s to 0.01 s), while oscillations around the Maximum Power Point (MPP) are decreased by 68.9% (from 18.5% to 5.76%). Furthermore, under partial shading conditions, the AFLC effectively avoids local maxima and extracts up to 15% more power. Overall, the proposed AFLC-based MPPT system offers superior tracking speed, accuracy, and stability, making it a highly effective solution for enhancing PV system performance in dynamic and real-world operating conditions.