Research on Online Parameter Identification Strategy for Brushless DC Motors Based on Model Reference Adaptive Algorithm

With the rapid advancement of industrial automation, brushless DC motors (BLDCMs) have gained widespread application across various domains, such as industrial production and household electronics. However, fluctuations in motor parameters caused by external conditions and load changes can adversely impact system stability and performance. To address these challenges, this paper introduces a novel online parameter identification approach based on the Model Reference Adaptive System (MRAS) algorithm, aiming to optimize motor parameter tracking in real time. Initially, a comprehensive mathematical model for BLDCMs is developed, and the effects of parameter variations on dynamic performance are analyzed in depth. Subsequently, a reference adaptive system is designed, employing real-time voltage and current signals as input data to dynamically adjust the motor model parameters for alignment with actual operational states. The simulation results validate the proposed method's effectiveness, showcasing its capability to achieve precise parameter tracking, swift dynamic response, and strong resilience in complex operating conditions, thus laying a solid foundation for high-accuracy motor control systems.