Research On Parameter Identification Method For Permanent Magnet Synchronous Motor

Permanent magnet synchronous motor(PMSM)has been widely used in many fields due to its advantages such as small size,high power density,high efficiency,and excellent dynamic performance.However,in the actual operation of the motor,its own parameters may change due to external factors such as temperature,resulting in improper design of the current loop and speed loop controller parameters of the PMSM vector control system,which affects its control performance.Therefore,real-time online identification of motor parameters is of great significance for improving motor control performance and real-time monitoring of its operating status.This paper focuses on the parameter identification technology of surface mounted permanent magnet synchronous motors from the following aspects:Firstly,the mathematical model of PMSM in natural coordinate system is introduced,and it is transformed into a two-phase synchronous rotating coordinate system through coordinate transformation.The decoupled PMSM mathematical model is obtained,simplifying the design steps.The vector control method and the principle of SVPWM modulation are introduced,and a permanent magnet synchronous motor system framework based on vector control is obtained.In view of the cost and robustness limitations of traditional phase current sensors,a single resistance phase current reconstruction control strategy based on vector phase shifting is analyzed.The above model is simulated and analyzed in MatLab/Simulink,laying a foundation for subsequent parameter identification research.Secondly,the influence of stator resistance,stator inductance,and rotor flux misalignment on the vector control system is analyzed,and a PMSM mathematical model with adjustable parameters is built to verify the above conclusions,leading to the necessity of parameter identification.Therefore,the principle and identification model of Model Reference Adaptive and Extended Kalman filter parameter identification algorithms are introduced in detail.The advantages and disadvantages of the two algorithms are compared through simulation.Thirdly,in view of the complexity of adjusting the adaptive rate parameters of the model reference adaptive algorithm,as well as the limitations of traditional Kalman filter algorithms in terms of computational complexity and high order error rejection,an improved Kalman filter parameter identification method based on Unscented Transformation is proposed for parameter estimation of permanent magnet synchronous motors,and its performance is compared with traditional algorithms,verifying the feasibility and superiority of the Unscented Kalman filter parameter identification method;This paper introduces the principle of Deadbeat Current Prediction controller,analyzes its performance advantages and disadvantages compared to traditional PI regulators,and analyzes the controller error and stability problems caused by parameter misalignment.On this basis,it proposes to use Unscented Kalman filter parameter identification to dynamically adjust the model parameters of Deadbeat Current Prediction,reducing its dependence on parameter accuracy.Simulation results show that the above algorithm has strong parameter robustness and dynamic current tracking ability.Finally,an experimental platform for three-phase PMSM speed control system based on the YXDSP-F28335 development board kit was built,and the algorithm described in this article was programmed and experimentally verified in the CCS software environment.The results show that the parameter identification algorithm based on Unscented Kalman filter can quickly and accurately identify the inductance and magnetic linkage parameters,and has excellent parameter adjustment ability for the current loop controller.