Alinea Brushless DC (BLDC) motors require sophisticated control systems to optimize their performance in modern industrial applications. This research develops a comprehensive mathematical model using state-space approach and designs a fuzzy logic control system for BLDC motors with experimental validation. The mathematical model was developed based on electrical and mechanical parameter identification through DC and AC testing, resulting in high-accuracy system dynamics representation. The fuzzy logic control system was designed using Mamdani structure with 49 rules and optimized using Chameleon Swarm Algorithm. Experimental validation was conducted using a test bench platform with 500W BLDC motor and DSP-based control system. Model validation results showed RMSE values of 15.3-28.9 rpm for speed and coefficient of determination R² reaching 0.963-0.987. The fuzzy logic control system demonstrated superior performance with rise time 45.2 ms, settling time 132.5 ms, overshoot 2.8%, and steady-state error 0.5 rpm compared to conventional PID controller. Tracking performance testing yielded RMS error 3.2 rpm for sinusoidal reference and robustness evaluation showed good resistance to load disturbances. This research successfully integrated accurate mathematical modeling with adaptive control system that can be implemented in various industrial applications requiring high-precision motor control. Keywords: BLDC motor, fuzzy logic, mathematical modeling, control system, experimental validation