Model predictive current control method for PMSM drives based on an improved prediction model

In motor drive systems based on model predictive control, a mathematic model of the motor is used to predict the future behavior of the system. However, the parameters in the motor model may not match their real values since these parameters may vary under different operation conditions. All parameter variations result in inaccurate predictions, and influence the steady-state control performance of the whole control system. In this paper, an improved model predictive control method is presented. Firstly, when parameter mismatches exist, the sources of the current prediction error are analyzed. It is revealed that current prediction error is directly affected by a prediction model with parameter mismatches and inaccurate one-step delay compensation. In particular, the influence form one-step delay compensation is discussed in this paper. Then a reaching-law-based sliding mode discrete observer is introduced to implement accurate one-step delay compensation and to observe all parameter variations. Finally, a predictive control method combined with sliding-mode discrete observation is presented to reduce parameter sensitivity. Simulation and experimental results show that the proposed method can increase the robustness of model predictive control systems.