Fractional order periodic adaptive learning compensation for cogging effect in PMSM position servo system

Fractional calculus is a generalization of the integration and differentiation to the fractional (non-integer) order. In this paper, we devised a fractional-order periodic adaptive learning compensation (FO-PALC) method for cogging effect minimization for permanent magnetic synchronous motors (PMSM) position and velocity servo system. Cogging effect is a major disadvantage of PMSM. In fact, the cogging force in PMSM is a position-dependent periodic disturbance. In our FO-PALC scheme, in the first trajectory period, a fractional order adaptive compensator for cogging effect is designed to guarantee the boundedness of all signals. From the second repetitive trajectory period and onward, one period previously stored information along the state axis is used in the current adaptation law together with a fractional order low pass filter. Both stability analysis and experimental illustrations are presented to show the benefits from using fractional calculus in periodic adaptive learning compensation for cogging effects in PMSM servo systems.