A novel power quality-improved high-step-up-gain Luo converter-powered BLDC motor drive with model reference adaptive controller for electric vehicles

This manuscript suggests an advanced high-step-up-gain Luo converter-fed brushless DC motor (BLDCM) drive for enhancing the power quality by using artificial neural network (ANN)-based model reference adaptive control algorithm. To overcome these issues, designing a competent intelligent controller by using an effective design approach is essential to utilize the brushless DC motor (DCM) to its maximum potential. The model reference adaptive controller system has a parameter modification tool added with the regular feedback loop which accord better results when there are modifications in process parameters. The ANN is significant tool for reckoning and controlling system, with the key factors of intrinsic parallelism, fault tolerance and learning fitness. To address the issue of adapting the nonlinearity, parameters and loads in BLDC motor drive, an integration of the MRAS with ANN features is employed. The proposed adaptive system-based speed control for BLDC motor drive system effectiveness is compared with existing PID-based speed control strategy by utilizing the MATLAB Simulink platform. However, the obtained result highlights the proposed model reference adaptive control (MRAC)-based model in optimizing BLDC motor drives for applications requiring high precision and adaptability.