Model reference adaptive permanent magnet synchronous motor sensorless vector control method based on super-twisted sliding mode algorithm

Abstract

The invention provides a model reference adaptive permanent magnet synchronous motor sensorless vector control method based on a super-twisted sliding mode algorithm, which belongs to the field of motor control, and solves the problems of poor stability, low precision and poor robustness of a permanent magnet synchronous motor sensorless vector control method in the prior art. According to the method, a reference model and an adjustable model of a permanent magnet synchronous motor position sensorless identification system are established based on a model reference adaptive system (MRAS), and a PMSM position identification system based on the MRAS method is constructed. A feedback correction link is added on the basis of MRAS, and the convergence speed of errors between output of a reference model and output of an adjustable model is increased. Meanwhile, a super-distortion sliding mode algorithm is introduced into the MRAS to replace a PI self-adaptive mechanism in the MRAS, so that the robustness of the system is improved. The dynamic stability and the robustness of the system can be improved. When perturbation occurs to the motor parameters, the influence on the accuracy of rotating speed estimation is small.

Description

technical field [0001] The invention relates to the field of motor control applications, in particular to a model reference adaptive permanent magnet synchronous motor position sensorless vector control method based on a super twisted sliding mode algorithm. Background technique [0002] Permanent magnet synchronous motor (PMSM) has many characteristics such as small size, high efficiency and good reliability, and has been widely used in various high-performance drive systems. Generally, the drive of permanent magnet synchronous motors requires mechanical sensors to detect the speed of the motor and the magnetic pole position of the rotor. The existence of mechanical sensors increases the cost and the complexity of the system. In order to overcome the adverse effects of traditional mechanical sensors on the control system, Researching a sensorless control method has become a research hotspot in the field of permanent magnet synchronous motor drive. Sensorless control techno...

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Problems solved by technology

[0003] The pulse vibration high-frequency injection method has a good identification effect when the motor is at low speed, but as the speed increases, factors such as high-frequency interference cause the injection method to be no longer suitable for motor position estimation

When the motor speed is high, the fundamental back EMF is large, and the position identification method based on the motor model can play a good role in making up for the shortcomings of the high-frequency injection method. Commonly used identification algorithms based on model points include SMO method, EKF method, Among them, the MRAS method is simple and easy to implement, and has a relatively strong ability to resist external interference, especially in the high-speed stage of the motor's stable operation. It has better steady-state accuracy and dynamic performance, but the traditional MRAS method needs to overcome the following shortcomings: traditional When MRAS is used for PMSM speed and position estimation, only when the difference between the output of the reference model and the adjustable model converges to zero, the estimated speed and rotor position can converge to their corresponding actual values, and a feedback correction can be added to it In order to speed up the convergence speed of the difference between the output of the reference model and the adjustable model, the dynamic performance of the system is improved; in addition, the PI adaptive mechanism inside the MRAS is not robust, and the introduction of the traditional sliding mode can improve its robustness , but it will cause the chattering problem and affect the smooth operation of the motor. Therefore, the introduction of the super-twist sliding mode algorithm not only increases the robustness of the system, but also alleviates the chattering problem of the traditional sliding mode

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