Predictive Current Control Of Permanent Magnet Synchronous Motor System Based On Oversampling

Permanent magnet synchronous motor is widely used in numerical control processing,industrial robots,aerospace and other fields.In the permanent magnet motor control system,the fast and stable current inner loop is the key factor to ensure the motor torque control performance.The traditional vector control current loop has a slow dynamic response,so some new control strategies such as deadbeat predictive current control are applied in the current loop.Deadbeat predictive current control uses the current running state and predictive model of the motor to predict the reference vector to be applied at the next moment and act on the motor.The dynamic response performance is good,but it depends on the accuracy of the motor parameters in the predictive model.In the actual working condition,the parameters of stator inductance,resistance and permanent magnet flux will change with the change of motor temperature and running state,and the parameter mismatch will seriously affect the control performance of the motor system.Therefore,this paper proposes an online parameter identification method based on Adaline neural network.This method does not need to inject d-axis current to identify the parameters during the operation of the motor.It only needs to make full use of the inherent phenomenon that the q-axis current changes with the load change during the operation of the motor to identify the parameters.Aiming at the problem that the parameter identification effect is easily affected by the motor speed,a new variable step neural network algorithm is designed.The speed factor is introduced into the step function to ensure the implementation effect of the identification algorithm at different speeds.This paper analyzes the main factors that restrict the dynamic and steady-state performance of the current loop,including the switch frequency limitation and calculation delay of the inverter,A/D sampling delay,duty cycle update delay and so on.These factors make the feedback current of the current loop unable to accurately track the reference value under the condition of low carrier wave ratio,thus limiting the torque output performance of the motor.Therefore,this paper proposes a deadbeat predictive current control method based on oversampling,which improves the sampling frequency and control frequency of the system without increasing the switching frequency of the inverter,and further improves the control performance of the system under the low carrier wave ratio condition through the asymmetric space vector modulation method.The proposed method is verified by experiments on the permanent magnet motor control system based on two-level inverter,and compared with the experimental waveform of the traditional deadbeat predictive current control method.The experimental results show that under the condition of parameter mismatching,the current static difference caused by the model parameter mismatching can be eliminated by the on-line parameter identification method proposed in this paper,and under the condition of low carrier wave ratio,the dynamic and steady-state performance of the current loop can be significantly improved by using the deadbeat predictive current control method based on over sampling.