Adaptive Robust Control for Maximum Power Point Tracking in Photovoltaic Systems based on Sliding Mode and Fuzzy Control

Abstract

Photovoltaic (PV) systems play a crucial role in renewable energy generation, but their efficiency heavily depends on accurate Maximum Power Point (MPP) tracking under varying environmental conditions. This paper applies an adaptive robust controller (ARC) to improve MPP tracking performance in PV systems, with a particular focus on enhancing robustness and reducing chattering. First, a sliding surface is defined based on the maximum power point. Then, a sliding mode controller is designed to ensure robustness against system uncertainties and external disturbances. To mitigate the chattering effect, a fuzzy logic-based controller is integrated into the ARC framework. The proposed controller is proven to be stable according to the Lyapunov criterion, providing robustness to uncertain parameters and external disturbances and reducing chattering. The proposed controller is validated through comparative simulations, demonstrating its superior performance over conventional methods. The results demonstrate that the proposed ARC achieves faster convergence, higher tracking accuracy, and improved robustness compared to conventional methods. Moreover, the integration of fuzzy logic significantly mitigates chattering, enhancing system efficiency and reliability. Given these advantages, the proposed controller is well-suited for real-world PV energy conversion systems, particularly in environments with rapidly changing irradiance and temperature conditions.