Improved Multi-step Model Predictive Current Control Of Permanent Magnet Synchronous Motor With Dead Time Optimization

As environmental problems and energy crisis become more and more urgent problems in the 21st century,new energy vehicles have gradually become an important part of the industry that cannot be ignored under the support of national policies.In order to meet the extensive demand of new energy vehicles in the market,permanent magnet synchronous motor has been widely used in the drive system of new energy vehicles due to its advantages of small size,large power density and wide speed range.As one of the speed regulation methods of permanent magnet synchronous motor,model predictive current control has been widely discussed and studied because of its advantages such as simple principle,easy to handle multi-variable control system containing constraints,and no weight coefficient.However,due to the local optimal problem of the single step predictive control,the vector selection has certain limitations.At the same time,there are few researches on the dead-zone problem in the model predictive current control,so it is urgent to analyze the dead-zone compensation problem in the model predictive current control.This thesis investigates an improved multi-step model predictive current control with dead time optimization for surface mounted permanent magnet synchronous motors.Firstly,based on the characteristics of single step prediction in model predictive current control,a multi-step model predictive current controller is introduced to improve the local optimal problem.However,there is still room for improvement in the steady-state performance of multi-step model predictive current control,because only one vector is applied within a control cycle.To solve this problem,extended voltage vector is introduced to expand the vector control set and improve the steadystate performance of current control.Meanwhile,for reducing the computation,a simplified vector optimization method is designed,and the fast vector selection method is used in the second step prediction to form an improved multi-step model prediction current control strategy.Secondly,for the propose of address the issue of dead-zone,a specific analysis of the dead-zone is carried out in the improved multi-step model predictive control,and the dead-zone is equivalent to a dead-zone voltage vector.Based on the detailed analysis of the impact of the dead-zone voltage vector on prediction error,the dead-zone voltage vector is classified and processed.Meanwhile,the dead-zone voltage vector action time that reduces prediction error is obtained through current deadbeat calculation.In this way,an improved multi-step model predictive current control with dead time optimization is constructed to reduce the stator current distortion rate and improve the current steady-state performance.In order to verify the correctness and effectiveness of the research method,multi-step model predictive current control,improved multi-step model predictive current control,and improved multi-step mode predictive current control with dead zone optimization were simulated and validated on MATLAB/Simulink simulation software.Secondly,the control strategy is verified experimentally on the permanent magnet synchronous motor towing experimental platform.The simulation and experimental results show that compared with the multi-step model predictive current control,the improved multi-step model predictive current control has better steady-state performance without increasing the computational complexity,and does not increase the switching frequency too much when improving the steady-state current performance.Then,compared with the improved multi-step model predictive current control with fixed dead time,the improved multi-step model predictive current control with dead time optimization can effectively reduce the stator current distortion.