Enhanced Maximum Power Point Tracking for Photovoltaic Systems Using Fuzzy Backstepping Sliding Mode Control and Particle Swarm Optimization

This paper presents an advanced MPPT control strategy for photovoltaic (PV) systems, combining Fuzzy Logic with Backstepping Sliding Mode Control (FBSMC) and optimized using Particle Swarm Optimization (PSO). The proposed FBSMC method aims to improve the efficiency and performance of PV systems under varying environmental conditions, such as fluctuating irradiance, temperature changes, and partial shading. By integrating fuzzy logic, the controller can dynamically adjust control parameters, leading to faster convergence to the Maximum Power Point (MPP), improved tracking accuracy, and reduced chattering compared to conventional methods like Perturb and Observe (P&O) and traditional Sliding Mode Control (SMC). Simulation results demonstrate that the FBSMC consistently outperforms these conventional approaches in terms of power output, settling time, and Mean Squared Error (MSE). The findings indicate that the FBSMC offers a reliable, efficient solution for real-world PV applications, contributing to the broader adoption of renewable energy technologies by ensuring optimal power extraction under dynamic conditions.