Implement Motor Speed Control Using Field-Oriented Control (FOC)

Field-Oriented Control (FOC), also known as vector control, is a technique used to control Permanent Magnet Synchronous Motor (PMSM) motors. FOC provides good control capability over the full torque and speed ranges. The FOC implementation requires transformation of stator currents from the stationary reference frame to the rotor flux reference frame (also known as d-q reference frame).

Speed control and torque control are the most commonly used control modes of FOC. The position control mode is less common. Most of the traction applications use the torque control mode in which the motor control system follows a reference torque value. In the speed control mode, the motor controller follows a reference speed value and generates a torque reference for the torque control that forms an inner subsystem. In the position control mode, the speed controller forms the inner subsystem.

FOC algorithm implementation requires real time feedback of the currents and rotor position. Measure the current and position by using sensors. You can also use sensorless techniques that use the estimated feedback values instead of the actual sensor-based measurements.

This figure shows the Field-Oriented Control architecture for PMSM.

This example implements the Field-Oriented Control technique to control the speed of a three-phase Permanent Magnet Synchronous Motor (PMSM). Field-Oriented Control (FOC), also known as vector control, is a technique used to control a PMSM to provide good control capability over the full torque and speed ranges. The FOC implementation requires transformation of stator currents from the stationary reference frame to the rotor flux reference frame. You also need real-time feedback of the rotor position to implement an FOC algorithm. You can measure the position feedback value by using either a sensor-based or sensorless approach.

This example uses the sensorless position estimation technique. You can select either the sliding mode observer or the flux observer to estimate the position feedback for the FOC algorithm used in the example.

The Sliding Mode Observer (SMO) block generates a sliding motion on the error between the measured and estimated position. The block produces an estimated value that is closely proportional to the measured position. The block uses stator voltages (Vα, Vβ) and currents (Iα, Iβ) as inputs and estimates the electromotive force (emf) of the motor model. It uses the emf to further estimate the rotor position and rotor speed. The Flux Observer block uses identical inputs (Vα, Vβ, Iα, Iβ) to estimate the stator flux, generated torque, and the rotor position.

Models

We provide these models to implement sensorless FOC for PMSM:

  • mcb_pmsm_foc_sensorless_f28069MLaunchPad

  • mcb_pmsm_foc_sensorless_f28379d

You can use these models for both simulation and code generation. You can use the open_system command to open a model. For example, use this command for a F28069M based controller:

open_system('mcb_pmsm_foc_sensorless_f28069MLaunchPad.slx');

For the model names that you can use for different hardware configurations, see the Required Hardware topic in the Generate Code and Deploy Model to Target Hardware section.

Required MathWorks Products

  • To simulate model:

    1. For the model: mcb_pmsm_foc_sensorless_f28069MLaunchPad

      • Motor Control Blockset™

      • Fixed-Point Designer™

    2. For the model: mcb_pmsm_foc_sensorless_f28379d

      • Motor Control Blockset

  • To generate code and deploy model:

    1. For the model: mcb_pmsm_foc_sensorless_f28069MLaunchPad

      • Motor Control Blockset

      • Embedded Coder®

      • Embedded Coder Support Package for Texas Instruments™ C2000™ Processors

      • Fixed-Point Designer

    2. For the model: mcb_pmsm_foc_sensorless_f28379d

      • Motor Control Blockset

      • Embedded Coder

      • Embedded Coder Support Package for Texas Instruments C2000 Processors

      • Fixed-Point Designer (only needed for optimized code generation)

Prerequisites

  1. Obtain the motor parameters. We provide default motor parameters with the Simulink® model that you can replace with the values from either the motor datasheet or other sources.

    However, if you have the motor control hardware, you can estimate the parameters for the motor that you want to use, by using the Motor Control Blockset parameter estimation tool. For instructions , see Estimate Motor Parameters by Using Motor Control Blockset Parameter Estimation Tool

    The parameter estimation tool updates the motorParam variable (in the MATLAB(R) workspace) with the estimated motor parameters.

  2. If you obtain the motor parameters from the datasheet or other sources, update the motor parameters and inverter parameters in the model initialization script associated with the Simulink models. For instructions to update the script, see Estimate Control Gains from Motor Parameters.

    If you use the parameter estimation tool, you can update the inverter parameters, but do not update the motor parameters in the model initialization script. The script automatically extracts motor parameters from the updated motorParam workspace variable.

Simulate Model

This example supports simulation. Follow these steps to simulate the model.

  1. Open a model included with this example.

  2. To simulate the model, click Run in the Simulation tab.

  3. To view and analyze the simulation results, click Data Inspector in the Simulation tab.

Generate Code and Deploy Model to Target Hardware

This section instructs you to generate code and run the FOC algorithm on the target hardware.

We provide a host and a target model. The host model is a user interface to the controller hardware board. You can run the host model on the host computer. The pre requisite to use the host model is to deploy the target model to the controller hardware board. The host model uses serial communication to command the target Simulink model and run the motor in a closed-loop control.

Required Hardware

This example supports these hardware configurations. Use the target model name (highlighted in bold) to open the model for the corresponding hardware configuration, from the MATLAB® command prompt.

  • LAUNCHXL-F28069M controller + BOOSTXL-DRV8305 inverter: mcb_pmsm_foc_sensorless_f28069MLaunchPad

  • LAUNCHXL-F28379D controller + BOOSTXL-DRV8305 inverter: mcb_pmsm_foc_sensorless_f28379d

For connections related to the preceding hardware configurations, see LAUNCHXL-F28069M and LAUNCHXL-F28379D configurations

Generate Code and Run Model on Target Hardware

  1. Simulate the target model and observe the simulation results.

  2. Complete the hardware connections.

  3. The model automatically computes the Analog-to-Digital Converter (ADC) or current offset values. To disable this functionality (enabled by default), update the value 0 to the variable inverter.ADCOffsetCalibEnable in the model initialization script.

    Alternatively, you can compute the ADC offset values and update it manually in the model initialization scripts. For instructions to compute the ADC offset, see Run 3-Phase AC Motors in Open-Loop Control and Calibrate ADC Offset.

  4. Open the target model for the hardware configuration that you want to use. If you want to change the default hardware configuration settings for the model, see Model Configuration Parameters.

  5. Load a sample program to CPU2 of LAUNCHXL-F28379D, for example, a program that operates the CPU2 blue LED by using GPIO31 (c28379D_cpu2_blink.slx), to ensure that CPU2 is not mistakenly configured to use the board peripherals intended for CPU1.

  6. Click Build, Deploy & Start in the Hardware tab to deploy the target model to the hardware.

  7. In the target model, click the host model hyperlink to open the associated host model. You can also use the open_system command to open the host model. For example, use this command for a F28069M based controller:

    open_system('mcb_pmsm_foc_host_model_f28069m.slx');

    For details about the serial communication between the host and target models, see Host-Target Communication.

  8. In the Host Serial Setup block mask of the host model, select a Port name.

  9. Update the Reference Speed value in the host model.

  10. Click Run in the Simulation tab to run the host model.

  11. Change the Start / Stop Motor switch position to On, to start running the motor.

    Note

    Do not run the motor (using this example) in the open-loop condition for a long time duration. The motor may draw high currents and produce excessive heat.

  12. Observe the debug signals from the RX subsystem, in the Time Scope of host model.

    Note

    If you are using a F28379D based controller, you can also select the debug signals that you want to monitor.