Research On Parameter Identification Algorithm Of Permanent Magnet Synchronous Motor

Permanent magnet synchronous motors have been widely used in many fields due to their advantages of easy control and high dynamic performance.However,the change of the motor parameters in the actual operating conditions of the motor will affect the performance of the control system,so obtaining accurate motor parameters plays an important role in the stable operation of the motor.Due to the problem of the under-ranking equation in the multi-parameter identification of the motor,and the influence of the nonlinear factors of the inverter,there is a certain error in the stator voltage acquisition during the parameter identification process.Therefore,this paper studies the above problems with the surface-mount permanent magnet synchronous motor as the main body.Firstly,the structure and classification of permanent magnet synchronous motor are introduced.The mathematical model of permanent magnet synchronous motor in common coordinate system is derived based on the principle of coordinate transformation.On this basis,the motor vector control algorithm is introduced.The closed-loop vector control simulation model based on id=0 is used to lay the foundation for the subsequent parameter identification algorithm design simulation.Then,in order to identify the problem of under-ranking of equations,this paper proposes a research strategy of step-by-step identification of parameters by applying model reference adaptive algorithm,and builds a simulation model to verify the simulation of step-by-step identification strategy.Considering the influence of the error voltage generated by the nonlinear factors of the inverter on the motor parameter identification,the motor voltage equation with error voltage is established to derive the mathematical model of the Adaline neural network parameter identification algorithm,and the variable step size LMS weight is proposed.The adjustment algorithm is used to solve the contradiction between convergence speed and steady-state error in the identification algorithm.Then the overall simulation model is established by combining the step-by-step identification strategy,and the effectiveness of the proposed variable-step Adaline neural network algorithm is simulated.At the end of this paper,the design of the experimental platform of the permanent magnet synchronous motor hardware system is introduced,and the proposed identification algorithm is experimentally verified.The comparison between the variable step size Adaline neural network algorithm and the traditional Adaline neural network algorithm parameter identification experiment results proves that the proposed algorithm can improve the convergence speed of parameter identification results and reduce the steady state error,and finally realize the accurate motor parameters.Identification.