The increasing penetration of photovoltaic (PV) systems into modern energy grids has made efficient energy extraction from solar panels a critical concern. Due to the nonlinear characteristics of PV systems and their strong dependence on environmental conditions (irradiance, temperature), achieving maximum power output at all times is not straightforward. Traditional MPPT techniques such as Perturb and Observe (P&O) or Incremental Conductance often fall short under dynamic or partially shaded conditions. To address these limitations, intelligent control strategies have emerged, leveraging soft computing methods, fuzzy logic, and artificial intelligence to enhance tracking performance. Furthermore, metaheuristic optimization algorithms, such as Particle Swarm Optimization (PSO) or Grey Wolf Optimizer (GWO) have shown promise in fine-tuning controller parameters for better adaptability and convergence. This research area lies at the intersection of control engineering, computational intelligence, and renewable energy systems, making it a fertile ground for innovative contributions in intelligent MPPT design.

The primary goal of this Research Topic is to explore and promote advanced MPPT (Maximum Power Point Tracking) control techniques for photovoltaic systems by integrating metaheuristic optimization, fuzzy logic, and artificial intelligence. The Topic aims to bridge the gap between classical control approaches and modern intelligent methods capable of handling real-world uncertainties such as partial shading, fast irradiance variation, and hardware limitations. By bringing together innovative algorithms, hybrid control architectures, and practical implementations, this Topic seeks to improve the efficiency, adaptability, and real-time performance of MPPT controllers. We particularly encourage submissions that introduce new or hybrid metaheuristic algorithms tailored for PV systems, AI-driven predictive controllers, adaptive fuzzy logic schemes, and experimental validations through simulations or hardware prototypes. Contributions that compare multiple intelligent techniques, propose benchmark datasets, or offer novel frameworks for embedded deployment are also welcome. Ultimately, this Topic aims to serve as a collaborative platform for researchers and practitioners working at the intersection of renewable energy, artificial intelligence, and optimization techniques.

This Research Topic invites original research articles, reviews, and case studies addressing intelligent MPPT control in photovoltaic systems. Themes include, but are not limited to:
• Metaheuristic optimization algorithms applied to MPPT (e.g., PSO, GWO, WOA, HHO, hybrid methods)
• Fuzzy logic and adaptive fuzzy controllers
• AI-based MPPT: neural networks, reinforcement learning, deep learning -Embedded or real-time implementation (e.g., FPGA, microcontroller)
• Comparative studies under partial shading or dynamic conditions
• Benchmarking and dataset generation for MPPT validation

Submissions should provide methodological innovation, performance evaluation, or practical validation that advances intelligent MPPT control and renewable energy integration.

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Intelligent MPPT Control for Photovoltaic Systems: Metaheuristic Optimization, Fuzzy Logic, and Artificial Intelligence Approaches