Improved Maximum Power Point Tracking in Photovoltaic Systems via Fuzzy Backstepping Sliding Mode Control and PSO Optimization

This paper presents a novel maximum power point tracking (MPPT) control strategy for photovoltaic (PV) systems that integrates fuzzy logic with backstepping sliding mode control (FBSMC), optimized via particle swarm optimization (PSO). The proposed FBSMC approach addresses the nonlinear and dynamic nature of PV systems under varying environmental conditions such as fluctuating irradiance, temperature variations, and partial shading. By embedding fuzzy logic into the backstepping sliding mode framework, the controller dynamically adjusts control parameters, significantly reducing chattering-a common drawback of traditional sliding mode control-while improving tracking accuracy and response speed. PSO is employed to systematically optimize the fuzzy controller parameters, thereby enhancing convergence speed and overall system performance without relying on heuristic tuning. Simulation results demonstrate that the FBSMC method outperforms conventional MPPT techniques such as Perturb and Observe (P&O) and standard sliding mode control in terms of power output, settling time, and mean squared error (MSE). The findings confirm that the proposed hybrid controller provides a robust, efficient, and reliable solution for real-world PV applications, facilitating optimal power extraction and contributing to the broader adoption of renewable energy technologies.