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@article{181809,
        author = {Dattatraya Raghoji and Sujatha Kumari and Kirankumar},
        title = {Artificial Neural Network-Controlled Resonant Converter for LED Applications},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {5507-5515},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=181809},
        abstract = {As a result of this research, a high-power-factor resonant LED driver under ANN control is proposed. Both the ANN and the buck power factor correction (PFC) unit rely on a single active switch, which is controlled by the ANN. Output current ripples are minimized by relying on a bulk capacitor and a quick voltage mode control loop in this setup. In spite of a little voltage ripple in pre-stage output bulk capacitor, the duty cycle of the active switch seems to be constant for the half-line cycle. Because the power factor improves when conduction mode is broken, Buck PFCs may be used in this context. The resonance between the resonant capacitor as well as the transformer's leaky inductor causes diodes in a resonance-based dc-dc unit to switch off at zero current. In each LED string, the output current can be balanced by balancing the charge in the resonant capacitor. Thus, the efficiency can be improved. The digital control is replaced by ANN control for improving the transient performance of the projected system. The proposed system is simulated in MATLAB/Simulink software.},
        keywords = {LED driver, Flicker-free, ANN, current balancing, power factor correction (PFC).},
        month = {June},
        }