Speed Control of BLDC Motor: Design and Simulation Based on Neural Network

Traditional speed control methods for BLDC motors often rely on conventional control algorithms, which may have limitations in terms of accuracy and adaptability. The neural network is designed to learn the nonlinear characteristics of the BLDC motor and adaptively adjust the control signals to achieve precise speed control. The neural network architecture is carefully constructed to capture the complex relationships between the motor inputs and outputs. The simulation environment is established using MatLab/SIMULINK, and the proposed neural network-based speed control system is implemented and tested. System performance is evaluated under various operating conditions, including different speed references. The simulation results demonstrate the effectiveness of the proposed neural network-based speed control system. The system exhibits superior performance in terms of accuracy, robustness, and adaptability compared to traditional control methods. It successfully maintains the desired speed in the presence of disturbances and load variations, showcasing the neural network's ability to learn and adapt. The results of this study expand BLDC motor control methods and may spur other advancements in the area of neural network-based control systems.