This paper proposes an intelligent position controller for brushless motor drives and motion controls. The controller is based on theory of the self-tuning tracking control. It integrates the principles of fuzzy logic with learning functions of neural networks into intelligent control architecture. A matrix formulation of a fuzzy-rule-based system is introduced. Consequently, a training-algorithm-based error function is also expressed in a matrix form. The resulting controller is significantly simple in structure and learning capability, and robust, and has high tracking performance (with respect to reference and measured data). With the proposed controller the rotor position can trace any arbitrary selected trajectory without overshooting or overstressing the hardware system. The entire system is designed and implemented in the laboratory using a hardware setup. The results of the laboratory testing are described in the paper. Compared to the proportional-plus-integral controller, the proposed controller yields a better dynamic performance with shorter settling time, without overshoot. Experimental results have shown that the proposed controller adaptively and robustly responds to a wide range of operating conditions.