Model predictive control (MPC) has been widely implemented in the motor because of its simple control design and good results.\nHowever, MPC relies on the permanent magnet synchronous motor (PMSM) system model. With the operation of the motor,\nparameter drift will occur due to temperature rise and flux saturation, resulting in model mismatch, which will seriously affect the\ncontrol accuracy of the motor. This paper proposes a model predictive control based on parameter disturbance compensation that\nmonitors system disturbances caused by motor parameter drift and performs real-time parameter disturbance compensation. And\nthe frequency-domain method was used to analyze the convergence and filterability of the model. The Bode diagram of\nmeasurement error and input disturbance was studied when the parameters were underdamped, critically damped, and\noverdamped. Guidelines for parameter selection are given. Simulation results show that the proposed method has good dynamic\nperformance, anti-interference ability, and parameter robustness, which effectively avoids the current static difference and\noscillation problems caused by parameter changes.
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