Abstract
The sensorless application of predictive control in drive applications has been investigated for a decade. Finite control set model predictive control (FCS-MPC) is one of the easy and practical methods in the predictive category. Several methods have been investigated for the sensorless application of FCS-MPC. Since the sensitivity of the predictive method to the speed error is more than that of the classical control methods, sophisticated speed estimators should be used in this method. The model reference adaptive system (MRAS) has been the most successful estimator. The main problem of this estimator is tuning the coefficients in different operating points and the stability of the adaptive function. The finite position set technique is a very recent solution. In this method, the adaptive function is used as the cost function and the optimum rotor position is selected by minimizing that. However, the numerous iteration is a barrier for application to the predictive method. Also, the application of the method for the synchronous reluctance motor (SynRM) is a challenge because of the lack of the rotor model as the adaptive function. In this paper, the finite position technique is modified for the predictive application. The number of iterations is reduced by an optimization method based on sensitivity analysis. Also, a new and simple function is used as the adaptive error function in order to apply the method to the sensorless control of the SynRM. The proposed method is evaluated by simulation and experiment.
Highlights
Synchronous reluctance motor has recently received much attention from researchers
Model predictive control has been successfully applied to power electronics and drives applications in the recent decade [3], [4]
One of the predictive methods used for control of the synchronous reluctance motor is
Summary
Synchronous reluctance motor has recently received much attention from researchers. The ability of this motor for high-temperature applications and high efficiency of this motor, high resistance against the centrifugal force are the reasons for this attention [1], [2]. These two works performed sensorless simplified FCS-MPC through a Luenberger observer for the induction motor This method has problems, especially in the low-speed range. Model reference adaptive system (MRAS) is one of the most successful observers that is presented in [22] This method is a model-based method and the flux or current error of the motor is used for estimating the position of the motor by means of a PI controller. Another method that does not use observer but can be placed in the first category is the optimization-based position sensorless control method that is presented in [21] In this method, the speed of the motor is calculated by transferring the model of the machine into an estimation reference frame. These problems are amended by the following improvements
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