Model-Free Predictive Direct Torque Control Of Permanent Magnet Synchronous Motor Based On Data Drive

Nowadays,with the continuous strengthening of national support for new energy related industries,the electric vehicle industry has developed rapidly.Permanent magnet synchronous motors are used as driving motors for electric vehicles by most domestic manufacturers due to their high efficiency,strong overload capacity,light weight,small size,good reliability and low cost.To enhance the overall efficiency of the motor control system,aside from enhancing the hardware efficiency of the motor and power electronic devices,the improvement of its control method is also crucial.Model predictive control has been widely used in motor control research in recent years due to its simple structure,convenient modeling,and good online optimization dynamic control performance.This article is based on model predictive control technology and conducts research on model free predictive torque control of permanent magnet synchronous motors.The focus is on optimizing the cost function of model predictive control and the stable operation of the motor under parameter mismatch conditions.Firstly,based on the establishment of a mathematical model,the basic principle of direct torque control technology was elaborated in detail.A direct torque control strategy for permanent magnet synchronous motors was designed based on hysteresis control technology,and simulation analysis was conducted.Next,attention is directed towards the combination of direct torque control technology with modern model prediction control technology,resulting in the derivation of the mathematical model for the model prediction direct torque control algorithm.A sequence model predictive direct torque control strategy is proposed to address the impact of weight factors on system performance.By changing the cost function structure,the impact of weight factors on system performance is eliminated.Simulation results demonstrate that the proposed sequence model predictive direct torque control strategy outperforms traditional model predictive torque control in terms of control effectiveness.Additionally,a ultra-local model was employed to enhance the torque prediction model of permanent magnet synchronous motors.Furthermore,a model-free predictive direct torque control strategy was suggested to mitigate the effects of motor parameter mismatch on system control performance.At the same time,based on data-driven thinking,recursive least squares method is used to adjust control system parameters online,further improving prediction accuracy and effectively improving the steady-state performance and antiinterference ability of the control system.The simulation results demonstrate that the proposed data-driven model free predictive control strategy can effectively overcome the impact of model parameter mismatch.Finally,based on the Microlab Box real-time simulation system,an experimental platform was designed and constructed to complete the programming and experimental verification of the three control strategies proposed in the article.Detailed analysis was conducted on the control performance of the three control strategies,with experimental results providing evidence of the effectiveness and superiority of the proposed control strategies.