Speed control is an important feature adopted for efficient and accurate speed and position control operations in the field of BrushLessDirect Current (BLDC) motor drives.The accuracy of the system's mathematical model determines the superiority of control performance. BLDC motors dominate the modern industry trend of using highly efficient and compact permanent magnet motors for a variety of applications.BLDC motors have also been identified by the electric vehicle industry as the most dependable and versatile motors for constantly changing industrial needs. It improves the performance of motion control system by modeling a precision brushless DC motor drive system. Therefore, this paper describes the brushless DC motor drive system that uses PID (proportionalintegral-derivative) fuzzy adaptive logic controller. The fuzzy control principle and its characteristics have been introduced. The principle of fuzzy rule-based PID control has been studied. In the case of dynamic systems, the model can be inferred adaptively in real time. This paper evaluates a BLDC motor's speed using raw data and Kalman filter data. The experimental results show that the inferred model closely matches the dynamic system's actual output. As a result, the described method appears to be promising for brushless DC motor control systems that have sparse feedback patterns.

PID controller, BLDC motor, fuzzy control, Speed regulation, Motor drive system.