Research On Model Predictive Control Strategy For Permanent Magnet Synchronous Motor

Permanent magnet synchronous motors are widely used in railway traction,aerospace,textile,military and other fields because of their simple structure,light weight,small size,and high power density.With the continuous advancement of production technology,the requirements for accuracy,rapidity,and stability of the permanent magnet synchronous motor AC servo system have gradually increased.Under this premise,traditional control methods have become more difficult to meet high-performance control requirements,and there is an urgent need for a high-performance AC motor control strategy to cope with more demanding control occasions.Among many advanced control technologies,model predictive control is widely used in the field of motor control because of its simple structure and easy handling of complex nonlinear systems.This paper takes permanent magnet synchronous motor as the control object to study the model predictive control strategy,and proposes an optimized model predictive torque control strategy.The main research contents are as follows:This article first explains the purpose and significance of the research on model predictive control of permanent magnet synchronous motor,and summarizes the control strategy of AC speed regulation system,as well as discusses the development status of model predictive control of permanent magnet synchronous motor based on relevant domestic and foreign literature.At the same time,the basic structure of the permanent magnet synchronous motor is introduced,the mathematical model of the permanent magnet synchronous motor in different coordinate systems is established,and the principles of the two traditional control strategies of vector control and direct torque control are explained.Then the model predictive control theory is introduced to mathematically describe the predictive model of the permanent magnet synchronous motor.On this basis,the traditional model predictive current control is introduced and the principle and specific implementation scheme of the traditional model predictive torque control are analyzed.Secondly,in order to eliminate the weight coefficient of the cost function in traditional model predictive torque control and further improve the control performance of traditional model predictive torque control,this paper proposes an optimized model predictive torque control strategy based on voltage vector expansion.This strategy constructs a reference stator flux vector based on the analytical relationship among torque,reference stator flux amplitude and rotor flux,to convert the separate control of torque and flux amplitude into flux vector control,thereby avoiding the cumbersome design of weighting coefficients.On the basis of delay compensation,the reference voltage vector is calculated online according to the deadbeat control principle.At the same time,the voltage vector control set is extended,two adjacent voltage vectors are determined according to the spatial position of the reference voltage vector,and the optimal duty cycle corresponding to the two adjacent voltage vectors and the zero vector is calculated by geometric relation,so as to realize the three-vector duty cycle control.It optimizes the control performance while eliminating the weight coefficient,and further simplifies the system structure.Finally,using Matlab/Simulink software to build a simulation model,the three control methods of direct torque control,traditional model predictive torque control,and optimized model predictive torque control are simulated and studied from two aspects of steady-state performance and dynamic performance.Based on the software and hardware implementation scheme of the permanent magnet synchronous motor control system,a permanent magnet synchronous motor drive control experimental platform with TI’s motor control dedicated DSP chip TMS320F28335 as the core is built to conduct experimental research on different control methods,and the feasibility and effectiveness of the proposed method were verified through simulation and experiment.