Neural Network Command Filtering Control Of Permanent Magnet Synchronous Motor Considering Iron Loss

In the vehicle AC drive system,permanent magnet synchronous motor has been widely used in the agriculture,industry and other fields because of its high power density,high energy conversion efficiency,wide range of speed and torque inertia ratio,dynamic characteristics and static characteristics and long service life etc.However,permanent magnet synchronous motor(PMSM)is a system with strong coupling,time-varying parameters,large disturbance and high nonlinearity.At the same time,it is common that the iron loss problem existing in permanent magnet synchronous motor drive system will affect the performance of the motor.Especially,in terms of energy,environmental protection and industrial significance,it will also affect the overall performance of certain motor drive objects such as electric vehicles.What's more,the traditional control strategy mostly studies the model of PMSM without iron loss.Therefore,optimizing the control strategy of PMSM drive system with iron loss has become a hot and difficult research point in the control field,which will affect the long-term development of electric vehicle industry.The research goal of this paper is the control strategy for permanent magnet synchronous motor drive system with iron loss.In order to solve some problems of permanent magnet synchronous motor control strategy of the classic,based on the command filtered backstepping technique,a new permanent magnet synchronous motor with iron loss of the adaptive neural network position and speed tracking control strategy is studied.The main research results are as follows:Firstly,an adaptive neural network tracking control strategy for nonlinear systems based on command filtering and backstepping is studied.With the neural network approximation properties to deal with unknown nonlinear function of the system,utilize the two order low-pass filtering to handle the virtual control function to overcome the “explosion of complexity” phenomenon by introducing the command filter technique,utilize backstepping method to construct the adaptive neural network controller,and utilize Lyapunov method to investigate the stability of the system.The controller can guarantee the boundedness and good tracking performance of the system.Secondly,the adaptive neural network position tracking controller for permanent magnet synchronous motor with iron loss is developed based on the command filter technology and backstepping control method.The neural network approximation characteristic is used to approximate the unknown nonlinear functions in the permanent magnet synchronous motor with iron loss,by command filter to eliminate the “explosion of complexity” phenomenon.The adaptive backstepping method is used to construct the real controller of the closed-loop system,which effectively overcomes the effects of system parameters and unknown load disturbance and other factors,and analyze the stability of the motor system according to Lyapunov theory.Compared with dynamic surface control method ensures that the position tracking controller based on command filtering technique can track the given signal quickly.Thirdly,the speed regulation controller of permanent magnet synchronous motor with iron loss is studied based on the command filter error compensation mechanism.The neural network approximation characteristic is used to approximate the unknown nonlinear functions in the permanent magnet synchronous motor with iron loss,by command filter to eliminate the “explosion of complexity” phenomenon.Error compensation is introduced to reduce the error caused by the command filter.Based on the Matlab/Simulink simulation platform,and all the signals in the closed-loop system are bounded with good control performance.