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Sensorless Field-Oriented Control of PMSM

This example implements the field-oriented control (FOC) technique to control the speed of a three-phase permanent magnet synchronous motor (PMSM). For details about FOC, see Field-Oriented Control (FOC).

This example uses the sensorless position estimation technique. You can select either the sliding mode observer or flux observer or extended EMF 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 $({V_\alpha },{V_\beta })$ and currents $({I_\alpha },{I_\beta })$ 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 $({V_\alpha },{V_\beta },{I_\alpha },{I_\beta })$ to estimate the stator flux, generated torque, and the rotor position.

The Extended EMF Observer block computes the electrical position, ${\theta _e}$ (or $\sin {\theta _e}$ and $\cos {\theta _e}$) in addition to the mechanical speed of PMSM from the measured voltage and current in the stationary alpha-beta reference frame.

To ensure that the detected rotor position is accurate, add the inverter board resistance value to the stator phase resistance parameter of the motor block and the stator resistance parameter of the Sliding Mode Observer, Flux Observer, and Extended EMF Observer blocks.

If you use flux observer, the example can run both PMSM and brushless DC (BLDC) motors.

The sensorless observers and algorithms have known limitations regarding motor operations beyond the base speed. We recommend that you use the sensorless examples for operations upto base speed only.


The example includes these models:

You can use these models for both simulation and code generation.

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®

  • C2000™ Microcontroller Blockset

  • Fixed-Point Designer™

2. For the model: mcb_pmsm_foc_sensorless_f28379d

  • Motor Control Blockset™

  • Embedded Coder®

  • C2000™ Microcontroller Blockset

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


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 PMSM Parameters Using Recommended Hardware.

The parameter estimation tool updates the motorParam variable (in the MATLAB® 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, see Estimate Control Gains and Use Utility Functions.

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.

Sliding Mode Observer parameters require tuning if you are using Sliding Mode Observer with the motor parameters estimated using the parameter estimation tool.

Simulate Model

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

1. Open a target model included with this example.

2. Use the Position Estimator button to select one of the following sensorless position estimation techniques:

3. To simulate the model, click Run on the Simulation tab.

4. To view and analyze the simulation results, click Data Inspector on 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.

This example uses 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 prerequisite 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. You can also use the target model name to open the model for the corresponding hardware configuration, from the MATLAB® command prompt.

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, 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, program that operates the CPU2 blue LED using GPIO31 (c28379D_cpu2_blink.slx), to ensure that CPU2 is not mistakenly configured to use the board peripherals intended for CPU1. For more information about the sample program or model, see the Task 2 - Create, Configure and Run the Model for TI Delfino F28379D LaunchPad (Dual Core) section in Getting Started with Texas Instruments C2000 Microcontroller Blockset (C2000 Microcontroller Blockset).

6. Use the Position Estimator button to select a sensorless position estimation technique.

7. Click Build, Deploy & Start on the Hardware tab to deploy the target model to the hardware.

8. In the target model, click the host model hyperlink to open the associated host model.

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

9. In the model initialization script associated with the target model, specify the communication port using the variable target.comport. The example uses this variable to update the Port parameter of the Host Serial Setup, Host Serial Receive, and Host Serial Transmit blocks available in the host model.

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


  • Before you run the motor at the required Reference Speed (by using either Sliding Mode Observer, Flux Observer, or Extended EMF Observer), start running the motor at 0.1 x pmsm.N_base speed by using open-loop control. Then transition to closed-loop control by increasing the speed to 0.25 x pmsm.N_base (where, pmsm.N_base is the MATLAB workspace variable for base speed of the motor).

  • High acceleration and deceleration may affect the sensorless position computation.

11. Click Run on the Simulation tab to run the host model.

12. Change the position of the Start / Stop Motor switch to On, to start running the motor in the open-loop condition (by default, the motor spins at 10% of base speed).

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.

We designed the open-loop control to run the motor with a Reference Speed that is less than or equal to 10% of base speed.

When you run this example on the hardware at a low Reference Speed, due to a known issue, the PMSM may not follow the low Reference Speed.

13. Increase the motor Reference Speed beyond 10% of base speed to switch from open-loop to closed-loop control.

NOTE: To change the motor's direction of rotation, reduce the motor Reference Speed to a value less than 10% of the base speed. This brings the motor back to open-loop condition. Change the direction of rotation but keep the Reference Speed magnitude as constant. Then transition to the closed-loop condition.

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


  • A high reference speed and a high reference torque can affect the Sliding Mode Observer block performance.

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

Other Things to Try

You can use SoC Blockset™ to implement a sensorless closed-loop motor control application that addresses challenges related to ADC-PWM synchronization, controller response, and studying different PWM settings. For details, see Integrate MCU Scheduling and Peripherals in Motor Control Application.

You can also use SoC Blockset™ to develop a sensorless real-time motor control application that utilizes multiple processor cores to obtain design modularity, improved controller performance, and other design goals. For details, see Partition Motor Control for Multiprocessor MCUs.