Research Of Model Predictive Control For Permanent Magnet Synchronous Motor

Conventional PI controller is generally used in permanent magnet synchronous motor.But it is difficult to adapt complex control requirements.And control effect is easily affected by the change of parameters. In order to improve the efficiency of the motor, the paper selected the model predictive control algorithm, which developed from the engineering. Model predictive control algorithm does not depend on the accuracy of the model parameters much. And it takes the past state and the future state information into account, taking the optimized value. So it is a control algorithm with good performance. But the model predictive control has disadvantage of large calculation, which restricts its application in motion control system. So it is needed to find relevant ways to overcome this drawback, to make it be better used in motion control systems. Therefore, the paper firstly considered how to reduce the amount of calculation. And in order to make appropriate compensation, then combined the fractional integral and sliding mode control. In order to make the permanent magnet synchronous motor control system has a better performance, the paper proposed a improved model predictive control algorithm.In this paper, the control scheme consists of three parts:(1) For how to reduce calculation of the model predictive control algorithm, the model predictive control algorithm was only applied to current loop control of permanent magnet synchronous motor. And the predictive model is the fixed discrete state equation of the motor current loop, without choosing online identification in each period. Next step is to simplify mathematical model, which is to remove coupling and interference terms.The system became a linear system. This greatly reduced the amount of calculation and it does not affect too much the advantage of the model predictive control. But it has some adverse effects on the accuracy and robustness of the control system.(2) For the model mismatch problem caused by poor precision of predictive model, the paper selected fractional integral which has a good robustness to compensate system error. The paper decided whether to add integral or not according to the changes of the error. When the error is zero or has a tendency to decrease, it can be removed. When error is increasing, add the integral. This way improved the accuracy of current loop control and system’s robustness.(3) Because model predictive control algorithm was only used in the current loop of permanent magnet synchronous motor. Therefore, in order to improve the performance of permanent magnet synchronous motor control system, speed loop also need a control strategy, which can improve the robustness of the system and does not have complex calculations. So the paper used the sliding mode control algorithm as a control method of permanent magnet synchronous motor speed loop. The chattering of sliding mode control need to be solved. In this paper, integral terminal sliding mode control can ensure that a given speed can track with no static error. And the system converged to equilibrium point in finite time. When added disturbance observer and expert control, the chattering of sliding mode control system with adjustable and reduced control law. Thus it met the needs of the speed control loop.The improved model predictive control algorithm was applied to permanent magnet synchronous motor control system. Through the Matlab/Simulink, the simulation results show that the control method reduced the amount of calculation of model predictive control algorithm under the condition of retaining the advantages. Compared to conventional PI control, the method has a better robustness, response speed and control precision. And model predictive control is derived from the engineering practice, so it can be better applied to practice.