Adaptive Model Predictive Control for Permanent Magnet Synchronous Motor

In order to solve the problem of speed fluctuation existing in PMSM model predictive control after loading, an optimization strategy based on model reference adaptation is proposed, which take torque and flux as state variables, constantly identified the actual inductance and flux parameters of the motor, and then compensated the adverse effects of parameter changes on the model predictive controller. It makes the whole control system form a complete adaptive optimization control. Secondly, the model prediction controller forms a closed loop between the motor and the controller through the identification algorithm, simplifying the problem of parameter matching and subsequent debugging.