Research On Parameter Identification And Speed Control System Of Permanent Magnet Synchronous Motor

Because of its high power density,wide speed range and strong practicability,permanent magnet synchronous motor has gradually become the first choice of driving motor for high performance speed regulation system.However,the system is easily affected by external disturbance in the actual speed regulation process,which leads to the change of motor parameters.In addition,the traditional PID control is difficult to meet the current high performance control requirements.In addition,as a motor speed regulation system,it also needs to obtain more accurate speed and position information.Therefore,this article put forward to improve the whale optimization algorithm to identify the motor parameters,and in view of the controller to improve the double closed loop speed regulation system,on the other hand,is based on the sliding mode observer to implement sensorless control of permanent magnet synchronous motor speed regulating system,the purpose is to make permanent magnet synchronous motor speed control system play a higher control performance.Firstly,depending on the type of the motor to determine the object of study for the table type permanent magnet synchronous motor,this thesis introduces the structure of permanent magnet synchronous motor speed regulating system,and based on coordinate transformation for the motor mathematical model of decoupling control and the design of speed loop and current loop controller,and then choose the right of the governing system of vector control strategy,through the above account of the speed control system,It lays a foundation for the research of some problems in the speed regulation system of permanent magnet synchronous motor.Secondly,to solve the problems of low precision and slow speed of PMSM parameter identification,an improved whale optimization algorithm was proposed to identify the motor parameters.Based on whale optimization algorithm,the mathematical model of analyzing the disadvantages of the algorithm,the introduction of Tent map mixed reverse learning strategies,nonlinear convergence factor and adaptive threshold to the improvement of the algorithm,the use of standard test functions to the improved optimization algorithm is evaluated,and deduces five order motor parameter identification model of full rank,the simulation results show that,The parameter identification effect realized by the improved algorithm is obviously better than the other two comparison algorithms,and it can realize the identification of five important parameters with high accuracy and fast response,such as the motor stator resistance,the dq axis inductance,the permanent magnet flux and the moment of inertia.Then,based on the standard ADRC model,an improved ADRC was designed.Since there are many parameters in the controller and it is difficult to set,the linearization scheme was used to simplify the structure of the tracking differentiator,and the structure of the extended state observer and nonlinear error feedback control law was optimized by using the sliding mode control method.The parameter identification of moment of inertia was introduced to improve the ADR controller.Similarly,based on the traditional ADRC controller,an improved ADRC controller is designed by introducing inductance parameter identification.The simulation results show that the improved controller can effectively suppress noise disturbance while simplifying parameter tuning,and improve the dynamic response and robustness of the speed regulation system.Finally,a senseless control scheme of PMSM speed regulation system with improved sliding mode observer is proposed.Since there is chattering in the estimation of the traditional sliding mode observer’s back electromotive force,a new saturation function is adopted as the switching function and the adaptive sliding mode gain is designed.The stability of the improved observer is determined according to Lyapunov theory.However,the system has phase delay when the low-pass filter works,so a low-pass filter based on the dynamic cutoff frequency is designed and combined with the Kalman filter to improve the system performance.Simulation results show the effectiveness of the proposed method.