Drive-Augmented model predictive controlwith Kalman state observer for long-strokemaglev planar motor

This paper proposes a model predictive control method formagnetic levitation planar motors with hysteresiscompensation of the drive system to achieve highresponsiveness and high accuracy long stroke positioningof planar motors. Based on the dynamics model of theplanar motor developed in the laboratory, and the explicit model of the drive system, including the driver and the coil,an augmented generalized model is built as a predictivemodel to predict the future position of the motor bycombining the actual measured position, state estimation,and perturbation estimation via a Kalman state observer,and an explicit analytical solution for optimal control isobtained by minimizing the cost function. Finally, theeffectiveness of the proposed model predictive controlmethod in realizing high-Accuracy time-varying positioningof magnetic levitation planar motors is experimentallyverified.