Design of an Optimal Proportional-Integral-Derivative Controller Utilizing AI Techniques for Brushless Direct Current Motor with Phase shift

The brushless direct current (BLDC) motor is a synchronous motor that features permanent magnets on the rotor and stator windings. Its applications are wide-ranging, including automotive systems, data storage devices, robotics, aerospace, home appliances, and instrumentation. The BLDC motor boasts numerous advantages over induction motors, including improved streamlined construction, small size, increased efficiency, minimal maintenance, reduced noise levels, and broad operating speeds range. There is a dire need for improvements in motor controllers in the future to meet specific application demands in terms of uncomplicated and economical designs. The speed range of BLDC motors can be shifted higher than the base speed by employing phase advance techniques. This capability makes it possible for BLDC motors to operate in many high-speed applications. Hence, designing an improved controller for performance in three-phase BLDC motor drives is crucial. The PID controller is widely employed in BLDC motor drives due to its effectiveness and low-complexity structure. The PID controller parameter tuning is critical to enhance the performance of motor control systems, particularly in cases involving nonlinearity and high inertia. It has been observed in previous studies that conventional control methods regulate phase advance BLDC motor systems, but conventional control methods tend to introduce steady-state errors and sluggish speed responses. Therefore, AI algorithm-based optimization of PID controller parameters is a promising approach to enhance the speed response of BLDC motor systems with phase advance. Surprisingly, the use of AI algorithms for tuning PID controllers in BLDC motor systems with phase advance has never been well explored in the current literature. AI algorithms have been widely accepted in controller design across numerous industrial applications. For instance, fuzzy logic has been applied to the design of BLDC rotor speeds, genetic algorithms (GA) have been applied to controller design, self-tuning PID controllers based on GA have been investigated, adaptive tabu search (ATS) has been applied to control synthesis, electric controls in aviation have been optimized via ATS, and current search (CS) methods have been employed for control synthesis. In this paper, implementation of AI algorithms to optimize the

کلیدواژه ها:

Brushless Direct Current Motor ، PID Controller ، AI Techniques ، Phase Shift ، Motor Control Systems