# Hybrid Fuzzy-DDPG Approach for Efficient MPPT in Partially Shaded Photovoltaic Panels
## 1 Introduction  
Partial shading is a prevalent issue in photovoltaic (PV) systems that significantly degrades performance by introducing multiple local maxima in the power-voltage (P-V) curve, thereby complicating the identification of the global Maximum Power Point (MPP) via conventional Maximum Power Point Tracking (MPPT) algorithms [1, 2, 3]. To overcome this challenge, researchers have increasingly employed advanced optimization techniques--particularly metaheuristic algorithms--to reliably locate the global MPP under non-uniform irradiance. Heuristic methods inspired by natural phenomena, such as the Flower Pollination Algorithm [4] and Genetic Algorithms [5], have demonstrated the capability to evolve candidate solutions toward global optimum performance. Additional approaches including Particle Swarm Optimization [6], Ant Colony Optimization, Simulated Annealing, Artificial Bee Colony, Firefly Algorithm, Cuckoo Search, and Differential Evolution [7] havebeen applied to MPPT; however, these methods often encounter high computational complexity, sensitivity to parameter tuning, slow convergence, and practical implementation constraints [8; 9]. Complementary to metaheuristic strategies, artificial intelligence (AI)-based techniques--such as fuzzy logic control and neural networks--have been proposed to address the MPPT challenge. Hybrid methods, for instance the integration of the shuffled frog leaping algorithm with the incremental conductance method [10], have enhanced global MPP tracking under partial shading. Nevertheless, these AI-based methods frequently require extensive training data, which increases memory usage and may lead to longer optimization times and transient power losses [11]. Classical control strategies, including Proportional-Integral-Derivative (PID) control [12; 13] and Sliding Mode Control (SMC) [14; 15], provide alternative solutions but often fail to manage the nonlinear dynamics and oscillatory behavior inherent in PV systems under partial shading. Other techniques such as distributed control, optimal control, backstepping, divide-and-conquer strategies [16; 17; 18], and simpler methods like Perturb and Observe (P&O) [19], have been explored with limited success [20; 21]. Recent advances in MPPT for photovoltaic systems under partial shading conditions have led to the development of several innovative control strategies. In [22], an adapted model predictive control algorithm is proposed to robustly track the global maximum power point by predicting future system states and optimizing converter duty cycles, thereby reducing reliance on multiple sensors. Meanwhile, ref. [23] introduces a bi-objective reinforcement learning framework for dynamic PV array reconfiguration that simultaneously maximizes power extraction and minimizes switching operations in the presence of moving clouds. Furthermore, Ref. [24] presents a novel approach that integrates fuzzy logic control with optimizable Gaussian Process Regression to enhance both tracking accuracy and convergence speed under complex environmental conditions. Finally, an adapted model predictive control algorithm has also been proposed to improve GMPP tracking under partial shading by minimizing the MPC cost function without relying on current sensors [25]. These contributions collectively underscore the potential of combining advanced control techniques with artificial intelligence to overcome the limitations of traditional MPPT methods. The research motivation behind this work is to develop an MPPT approach that overcomes these limitations by reducing the training burden and enhancing convergence speed. Reinforcement learning (RL) has emerged as a promising paradigm for MPPT under partial shading, with methods such as Deep Deterministic Policy Gradient (DDPG) and its variants effectively managing continuous action spaces [26; 27; 28; 29; 30; 31; 32]. However, the substantial data and computational resources required by these methods, along with their sensitivity to reward function design, remain critical challenges.