Advanced Anfis-Based Maximum Power Point Tracking for Solar Photovoltaic Systems: A Comparative Study with Deep Learning and Real- Time Implementation

Solar photovoltaic (PV) capacity is expanding rapidly, yet real-world energy yield still hinges on how reliably controllers track the maximum power point under disturbances such as partial shading, fast irradiance ramps, sensor noise, and embedded hardware limits. This review evaluates three intelligence families for MPPT: Adaptive Neuro-Fuzzy Inference Systems (ANFIS), Deep Learning (DL), and Reinforcement Learning (RL)through a deployment lens rather than simulation alone. Using a structured search (2018–2025) across major databases, we prioritised studies with processor- /hardware-in-the-loop (PIL/HIL) or embedded MCU/FPGA validation, and judged methods on four discriminating metrics: (i) global-peak hit rate under shading, (ii) convergence time and overshoot, (iii) steady-state power ripple, and (iv) edge feasibility (number format, latency, resources), alongside interpretability and audit requirements. Findings show ANFIS as the risk-adjusted frontrunner in non-benign conditions: compact, fixed-point designs consistently deliver millisecond-scale settling and ~99–100% tracking in dynamic tests, while hybrids (e.g., ANFIS-PSO/GEP or with nonlinear scaffolds) further suppress ripple and improve global-peak discovery. DL/RL can match or exceed ANFIS when rich sensing, compute headroom, and mature ML governance exist, but their gains are contingent on data pipelines, quantisation/latency engineering, safe exploration, and explainability. We recommend a SIL→PIL→HIL rollout, energy-weighted metrics under standardised shading/ramp scripts, and deploying a lean, auditable ANFIS now graduating DL/RL where HIL-proven advantages justify their operational complexity.