Reinforcement learning based fractional fuzzy controller for photovoltaic systems

Abstract

This article presents an innovative artificial nonlinear control technique for photovoltaic (PV) systems. A boost converter extracts maximum power from the PV system under different weather conditions. A fuzzy logic maximum power point (MPP) finding algorithm is used to determine the reference voltage of the PV system under various atmospheric conditions. It is assumed that the nonlinear dynamic model, parameters, and uncertainties of the converter are unknown. To estimate a precise model that contains nonlinearities, model uncertainties, and disturbances of the converter, proximal policy optimization (PPO) with a special reward function is applied. PPO is optimal in policy learning and sampling, fast to implement, and is a simple reinforcement learning (RL) algorithm. Then, estimations of dynamics and uncertainties are used to enhance the robustness of the suggested fractional order sliding mode controller. Furthermore, the controller coefficients are optimally adjusted by the PPO algorithm. The Lyapunov theorem is used for proving the control system's closed-loop stability. Also, the proposed methods can be generalized and used for other control systems. The simulation is conducted by MATLAB to evaluate the suggested controller's performance effectiveness. Robustness against the presence of parameter uncertainty is investigated. Moreover, the suggested technique is also compared with sliding mode controller.