Research On Model Predictive Current Control Of Synchronous Reluctance Motor

The synchronous reluctance motor has the advantages of no permanent magnet in the rotor,simple structure,good performance in complex and harsh working conditions,low processing difficulty,and low price.At present,synchronous reluctance motors have good applications in some occasions that require low-cost and high-performance motors,such as wind power motors and water conservancy motors,and have good development prospects in the motor market.However,the disadvantages of low power factor and large torque ripple of synchronous reluctance motor make the control strategy a key factor affecting its performance.Therefore,a high-performance control strategy is very important to improve its performance efficiency.Model predictive control has better dynamic performance than vector control and is better than direct torque control in steady state performance,and there is still a lot of room for development.This thesis mainly studies the model predictive current control of synchronous reluctance motors to obtain better performance than traditional control strategies.In order to study the performance of synchronous reluctance motor,this thesis establishes the mathematical model of synchronous reluctance motor in the two-phase rotating coordinate system,studies the maximum torque per ampere strategy distribution strategy of synchronous reluctance motor,and then analyzes the influence of current saturation on dq axis inductance Influence,and on this basis,the maximum torque per ampere strategy is optimized.In order to design a good control method,this thesis focuses on the analysis of the basic principles of model predictive control,using the previously obtained synchronous reluctance motor mathematical model to derive the predictive control predictive equation,respectively constructing the traditional single-vector model predictive current control,duty cycle The model predicts the current control and the generalized double vector model predicts the objective function of the current control,and compares and analyzes the three kinds of predictive controls,and studies the influence of increasing the number of action vectors on the control performance.In order to further obtain better performance than vector control,this thesis proposes a low switching frequency modulation model predictive current control strategy,which has two effective vectors and one zero vector in one control cycle to obtain a performance comparable to traditional vector control.Steady-state performance,while using model predictive control,the characteristics of multiobjective constraints and the reduction of switching losses can be added to the value function of the control to improve the control performance.Through the simulation,the control effects of the double vector model predictive current control,the traditional vector control and the low switching frequency modulation model predictive current control are compared and analyzed.Finally,through the hardware-in-the-loop hardware-in-the-loop simulation experiment platform of synchronous reluctance motor based on DSP and RT-LAB,the current control based on the duty cycle model,the generalized double vector model prediction current control,the traditional vector control and the low switching frequency modulation model prediction The experimental analysis of current control proves the superiority of low switching frequency modulation model predictive control over vector control,and verifies the feasibility of this scheme.