<p>This paper proposes the fuzzy cerebellar adaptive neuro (FCAN) intelligent controller, a novel hybrid control system designed to enhance the performance of a brushless direct current (BLDC) motor. Inspired by the adaptive capabilities of the human cerebellum, the FCAN intelligent controller seamlessly integrates the learning mechanisms of the cerebellum with the decision-making ability of fuzzy logic to attain superior dynamic control performance. The cerebellum controller, inspired by the human brain, has extensive applications in controlling non-linear and dynamic systems. It offers stable control and overcomes the limitations of conventional controllers like proportional-integral (PI). However, despite its robust design, the cerebellum controller suffers from the inability to accurately estimate sensory information. Its performance deteriorates when sensory signals are inaccurate. To address this, fuzzy logic is integrated into the cerebellum controller, enhancing its learning characteristics, resulting in the FCAN intelligent controller. The proposed controller is implemented on a BLDC motor drive system to optimize its performance and its efficacy is comprehensively evaluated under various operating conditions, such as constant speed and constant torque, constant speed and variable torque, and constant torque and variable speed scenarios. A comparative analysis is conducted with a cerebellum controller and a conventional PI controller to validate the effectiveness of the FCAN control approach. The simulation results reveal that the FCAN intelligent controller outperforms the cerebellum controller and PI controller in several key metrics. It effectively minimizes the settling time, rise time, overshoot, initial transient torque peak, current total harmonic distortion and enhances robustness against system disturbances and uncertainties.</p>