Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{202110,
        author = {TOFIK LATIF MULANI and NAJIYA ISSAK SHAIKH and Prof.Suraj S.Shinde},
        title = {A Review Paper on Soft Computing Based Hybrid MPPT Controller using ANN and INC Method},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {6025-6029},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=202110},
        abstract = {The increasing global demand for clean and sustainable energy has accelerated the deployment of photovoltaic (PV) systems. However, the inherently nonlinear characteristics of PV modules and their strong dependency on environmental factors such as irradiance and temperature pose significant challenges for achieving maximum power extraction. Maximum Power Point Tracking (MPPT) techniques are therefore critical for enhancing the overall efficiency and reliability of PV power conversion. Conventional MPPT algorithms, such as the Incremental Conductance (INC) method, offer simplicity, good stability, and satisfactory tracking under uniform conditions. Nevertheless, their performance tends to degrade under rapidly changing weather and partial shading scenarios due to oscillations around the maximum power point and slower dynamic response. To overcome these limitations, soft-computing approaches, especially Artificial Neural Networks (ANN) have emerged as advanced alternatives capable of learning nonlinear system behavior and providing adaptive control. This paper presents a soft-computing-based hybrid MPPT controller that strategically combines the predictive capabilities of an ANN with the robustness of the INC algorithm. The proposed ANN model is trained using a comprehensive dataset comprising PV voltage, current, irradiance, and temperature to predict the optimal duty cycle corresponding to the maximum power point. During real-time operation, the ANN serves as the primary estimator, offering rapid convergence and minimizing steady-state oscillations. The INC algorithm functions as a secondary corrective mechanism, ensuring accurate fine-tuning whenever the ANN output deviates due to unforeseen or abrupt environmental changes. This hybrid configuration leverages the strengths of both methods: the adaptability and fast response of ANN with the reliability and error-checking capability of INC.},
        keywords = {MPPT, ANN, INC, Soft Computing, PV system, Solar Energy, Hybrid MPPT Controller, DC-DC converter, Intelligent Control Techniques.},
        month = {May},
        }