Robust Model Predictive Flux Control of PMSM Drive Using a Compensated Stator Flux Predictor

Abstract

In the model predictive control of permanent magnet synchronous motor (PMSM), predictive flux control (PFC) has been widely studied because it does not need the complicated setting of weight factor. In predictive flux control, the prediction of stator flux vector is usually based on the voltage model. However, the mismatched resistance parameter and current sampling errors will lead to stator flux prediction errors, resulting in the degradation of control performance and even the system instability. To solve this problem, this paper proposes a compensated stator flux predictor based-predictive flux control (CSFP-PFC) of permanent magnet synchronous motor. In the proposed method, stator flux vector is firstly expressed at the rotor reference frame. Then, a compensated stator flux predictor is firstly constructed based on stator current prediction error. The accurate prediction of stator flux vector will ensure the selection of optimal voltage vector. Experimental results verify the effectiveness of the proposed algorithm.

Optimal weighting factor design based on entropy technique in finite control set model predictive torque control for electric drive applications

“…Weighting factor removal by reference transformation 33,34 Higher computational burden as compared with conventional PTC and difficult to incorporate multiple control objectives 35 Weighting factor tuning based on coefficient of variation 36 Optimized weights are uncertain in this method and complex calculations are required to implement on hardware Weighting factor tuning based on TOPSIS and NSGA-II methods 37 TOPSIS and NSGA-II algorithms require complex calculations leading to computational challenges 12 Weighting factor removal by Ranking method 38 Ranking based techniques become unfeasible as number of control objectives increases 39 Tuning of weighting factor based on simple additive technique 40 Although technique is simple but not suitable for multiple control objectives 11 Weighting factor tuning based on current ripples 41 Highly dependent on parameter estimation 8,42 Tuning of weighting factor based on error of control objectives 43 This method becomes challenging and complex when number of control objectives increases 44 Weighting factor tuning using Genetic Algorithm (GA) 45 , Simulated Annealing (SA) 42 or Gravitational Search Algorithm (GSA) 43 , Artificial Neural Network 46 , Ant colony based optimization 47 These algorithms are very complex and pose computational challenges 48 Weighting factor tuning based on algebraic/numerical techniques 49 Design complexity increases as slection of weighting factor increases 50 Weighting factor selection based on homogeneous cost functions [51][52][53] This technique is relatively efficient but unable to include multiple control objectives 54 Direct vector selection based techniques to remove weighting factors from cost function 55,56 Direct vector selection techniques provid lower computational burden and lower complexity , however cannot incorporate multiple control objecitve 57 Weighting factor elimination by using cascaded structure of FCS-MPC …”

A Review on Weighting Factor Design of Finite Control Set Model Predictive Control Strategies for AC Electric Drives

“…1) MPCs with Unifying Cost Functions: Model predictive flux control (MPFC) [65], [66], model predictive power control (MPPC) [67], [68], and model predictive active/reactive torque control (MPARTC) [69], [70] are designed to eliminate the WFs in MPTC. MPFC has been applied to electric drives in many studies, but there are a limited number of papers using MPPC in electric drives despite their popularity in power converters.…”

Improved model predictive control of permanent magnet synchronous motor based on lumped disturbance compensation

“…have become hot spots in research [1][2][3][4], in which motors play an important role. Currently, permanent magnet synchronous motors are widely researched and applied due to their advantages such as small size, high power density, and high efficiency [5][6][7][8].…”