Application and Optimization of Information Technology in Brushless Motor Control System

Abstract

Owing to their high efficiency, reliability, and superior precision in torque and speed regulation, Brushless DC (BLDC) motors have become a cornerstone technology across a diverse spectrum of applications, including industrial automation, electric vehicles (EVs), robotics, and consumer appliances. The ongoing and rapid evolution of information technology (IT), encompassing domains such as artificial intelligence (AI), the Internet of Things (IoT), big data analytics, and advanced computational methods, is fundamentally reshaping the landscape of motor control. This technological convergence presents a pivotal opportunity to transcend the limitations of conventional control paradigms, which often rely on fixed-parameter controllers and exhibit limited adaptability to dynamic operational conditions and system nonlinearities. Consequently, these legacy systems may not fully exploit the motor's potential, leading to suboptimal performance, reduced energy efficiency, and compromised reliability under variable loads and unforeseen disturbances. This paper seeks to conduct a comprehensive investigation into the integration and application of these emergent information technologies within BLDC motor control systems, with the overarching objective of proposing a coherent framework of optimization strategies. We systematically explore the implementation of AI-based techniques, specifically fuzzy logic and neural network controllers, to achieve adaptive, self-tuning control that enhances dynamic response and robustness. Furthermore, the role of IoT-enabled connectivity for real-time system monitoring, predictive maintenance, and data-driven operational optimization is critically examined. The paper also delves into the utilization of high-fidelity modeling and simulation, facilitated by IT tools, for refining control algorithms and conducting virtual prototyping, thereby reducing development time and cost. The proposed optimization strategies are analytically evaluated based on their capacity to significantly improve key performance metrics: overall system performance (encompassing dynamic response and tracking accuracy), energy efficiency across a wide power band, long-term cost-effectiveness through reduced maintenance and downtime, and operational reliability in demanding environments. The synthesis of these IT-driven approaches is posited to represent a significant leap forward, paving the way for the next generation of intelligent, connected, and highly efficient BLDC motor drive systems that are capable of meeting the escalating demands of modern industrial and consumer applications.