Study On Finite Contorl Set Model Predictive Current Control Of Permanent Magnet Synchronous Motor

The performance of the Permanent Magnetic Synchronous Motor(PMSM)is closely related to the control algorithm used to control it.However,the traditional current loop control algorithm is used to achieve AC-DC current decoupling control and anti-saturation.When dealing with the nonlinear constraints of the system,the short board is gradually exposed,which makes the dynamic response of the system weak.With the continuous exploration of predictive control technology,this paper focuses on the finite control set model predictive control with good dynamic performance as a motor current loop controller.First,a three-phase PMSM is subjected to the Clark and Park equivalent transformation to obtain a mathematical model in a two-phase synchronous rotating coordinate system,in order to release the coupling between the excitation component and the torque component of the motor.Based on this,combined with the rotor magnetic field orientation strategy,a dual closed-loop vector control system for PMSM and its simulation model are established.Secondly,focusing on the principles and characteristics of the finite control set model predictive current control,a derivation of the finite control set model suitable for PMSM predictive current control is derived.A fast optimal voltage vector selection method is used to improve the traditional prediction algorithm.The purpose is to improve the calculation efficiency of the current prediction algorithm,so as to quickly obtain the optimal control amount.The comparison of simulation results proves that the algorithm’s deduction and actual results are correct and effective.Thirdly,the dynamic and static performance of the prediction algorithm in the case of model mismatch is discussed from the perspective of coping with changes in motor parameters.Based on this,a robust current predictive control algorithm is introduced,which relies on the means of introducing past predicted values with weight coefficients during the prediction stage to widen the stable interval of the algorithm.When adjusting the weight coefficient,it is necessary to consider both the dynamic and static performance of the algorithm.Simulation results show that the robust current prediction algorithm can greatly improve the sensitivity of the algorithm to motor parameters.Finally,a finite control set model predictive current control algorithm based on Luenberger disturbance observer is introduced.By designing the Luenberger disturbance observer,the system disturbance is estimated in real time and introduced into the prediction model to find the optimal voltage vector.By plotting the system’s root trajectory and Bode plot curve for quantitative analysis,the algorithm achieves the goal of eliminating the static current difference on the basis of balancing the control bandwidth and stability of the system.The simulation results show that the real-time compensation of the system disturbance can effectively eliminate the steady-state current error.