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# Model Predictive Control of PMSM

Model Predictive Control of PMSM

This video explains the model predictive speed and torque control of PMSM in MATLAB simulink. also, explain the simulation results with the change in reference speed conditions and change in torque conditions.

## Introduction

Permanent magnet synchronous motors (PMSMs) are widely used in various industrial applications due to their high efficiency, low noise, and low maintenance requirements. Model Predictive Control (MPC) is a powerful control strategy that has been increasingly adopted for controlling PMSMs. In this article, we will discuss the basics of PMSMs and MPC, and how MPC can be used to control PMSMs.

### What is a PMSM?

A PMSM is a type of synchronous electric motor that uses permanent magnets embedded in the rotor to create a magnetic field. The stator, which contains the copper windings, generates a rotating magnetic field that interacts with the permanent magnets on the rotor to produce torque. PMSMs are widely used in various applications, including electric vehicles, wind turbines, and industrial machinery.

### What is MPC?

MPC is a model-based control strategy that predicts the future behavior of a system using a mathematical model and uses this prediction to generate a control signal. The MPC algorithm optimizes a cost function that reflects the desired performance of the system and generates the control signal that minimizes this cost function. MPC is widely used in various applications, including process control, robotics, and automotive control.

## PMSM Control Strategies

There are various control strategies that can be used to control PMSMs, including Field-Oriented Control (FOC), Direct Torque Control (DTC), and MPC. FOC is the most commonly used control strategy for PMSMs, but it has some limitations, such as sensitivity to parameter variations and non-linear behavior at high speeds. DTC is another popular control strategy that offers fast torque response and reduced sensitivity to parameter variations, but it has some limitations, such as high torque ripple and high switching frequency.

## Basics of MPC

MPC is a model-based control strategy that uses a mathematical model of the system to predict the future behavior of the system and generates a control signal that optimizes a cost function. The basic steps of the MPC algorithm are as follows:

1. Define the system model: The system model is a mathematical model that describes the behavior of the system. The system model can be derived from the physical equations that govern the behavior of the system.

2. Define the prediction horizon: The prediction horizon is the time period over which the MPC algorithm predicts the future behavior of the system. The prediction horizon is typically shorter than the sampling time of the control signal.

3. Define the control horizon: The control horizon is the time period over which the MPC algorithm generates the control signal. The control horizon is typically shorter than the prediction horizon.

4. Define the cost function: The cost function is a mathematical function that reflects the desired performance of the system. The cost function typically includes terms that penalize deviations from the desired trajectory, control effort, and state constraints.

5. Solve the optimization problem: The MPC algorithm solves an optimization problem that minimizes the cost function subject to the constraints of the system model.

6. Generate the control signal: The MPC algorithm generates the control signal that minimizes the cost function using the solution of the optimization problem.

## MPC for PMSM Control

MPC can be used to control PMSMs by using a mathematical model of the motor and optimizing a cost function that reflects the desired performance of the system. The PMSM model typically includes the electromagnetic equations, the mechanical equations, and the electrical equations. The cost function typically includes terms that penalize deviations from the desired trajectory, control effort, and state constraints.

MPC offers several advantages over other PMSM control strategies, including the ability to handle parameter variations and non-linear behavior, fast response to changes in the desired trajectory, and reduced torque ripple.