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Model Predictive Control of PMSM

Model Predictive Control of PMSM

Greetings, viewers! Today at LMS Solution, we delve into the intricate world of Model Predictive Control (MPC) applied to Permanent Magnet Synchronous Motors (PMSM). This simulation model is meticulously crafted to illustrate the dynamics of MPC in managing a PMSM with a DC source voltage of 500 volts.


System Components:

  1. PMSM Specifications:

  • Rated torque: 0.8 Nm

  • Rated voltage: 300 volts

  • Rated speed: 3000 RPM

  1. Electrical Parameters:

  • Stator resistance

  • Mutual inductance

  • Flux linkage

  • Permanent magnet signals

  1. Control System:

  • MPC Controller

  • Reference speed command (100 rad/s to 125 rad/s)

  • Stator current measurements (I_d, I_q)

  • Rotor angle measurement

  • Clock input

  • Electrical rotation angle (30 electrical degrees)

MPC Workflow:

  1. Initialization:

  • Initialize PMSM parameters (inductance, resistance, flux linkage).

  • Set the DC voltage to 500V.

  • Define the number of poles and calculate the electrical rotation angle.

  1. Prediction and Cost Function:

  • Predict I_d and I_q based on eight switching patterns.

  • Formulate a cost function to minimize the error between predicted and reference values.

  1. Optimization:

  • Minimize the cost function to determine the optimal switching pattern.

  • Obtain the index of the minimum cost.

  1. Voltage Pulse Generation:

  • Generate voltage pulses for the inverter based on the optimal switching pattern.

  • Apply the pulses to control the PMSM.

Simulation Results:

  1. Speed Control:

  • Reference speed initially set at 100 rad/s.

  • Changes to 125 rad/s after 0.05 seconds.

  1. Torque Control:

  • Electromagnetic torque shifts from 0.3 Nm to 0.6 Nm.

  • Stator current adjusts accordingly.

  1. Performance Evaluation:

  • Speed smoothly tracks the reference.

  • Torque responds to load changes.

  • MPC successfully maintains desired motor behavior.

Conclusion:

The simulation demonstrates the effectiveness of Model Predictive Control in regulating the speed and torque of a PMSM. The algorithm anticipates future states, optimizes control actions, and adapts to dynamic changes. This robust control methodology showcases its potential for real-time applications in electric motor systems.

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