Uploaded October 2025 | Updated September 2026, 3 weeks ago
Learn how to estimate the rotor position of a permanent magnet synchronous motor (PMSM) using an AI-based virtual sensor, eliminating the need for physical speed/position sensors. The approach leverages deep learning to generate, train, test, and deploy a neural network that predicts the rotor’s electrical position from voltage and current measurements.
Walk through the complete workflow using a Motor Control Blockset™ reference example:
• Generate training data through field-oriented control simulation.
• Create and train the neural network with Deep Learning Toolbox™.
• Validate the trained network using simulation.
• Generate code and deploy the trained virtual sensor to target hardware.
This demonstration is for motor control engineers seeking AI-based alternatives to traditional sensors, offering a scalable and cost-effective approach to rotor position estimation.
See example: bit.ly/3VQ3IOV
Motor Control Workflow with Single Deployment on Microchip MCU: bit.ly/48bNRSg
What Is Sensorless Brushless Motor Control? bit.ly/3IkiwCr
Optimal Torque Control Workflow for PMSM: bit.ly/41PB5Fc
Detect Unbalanced Motor by Using Neural Network: bit.ly/48aJcjp
Chapters:
00:00 Introduction
00:26 Quick Look: Virtual Sensor Demo
00:59 Sensorless Control for PMSM
02:01 AI-based Position Estimation Workflow
03:16 Example Walkthrough :Generate Data
07:50 Train & Test Neural Network
10:21 Embedded AI: Code Gen & Deploy
11:54 Hardware Demonstration
16:37 Conclusion
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Get a free product trial: goo.gl/ZHFb5u
Learn more about MATLAB: goo.gl/8QV7ZZ
Learn more about Simulink: goo.gl/nqnbLe
See what's new in MATLAB and Simulink: goo.gl/pgGtod
© 2025 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc.
See mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
Learn how to estimate the rotor position of a permanent magnet synchronous motor (PMSM) using an AI-based virtual sensor, eliminating the need for physical speed/position sensors. The approach leverages deep learning to generate, train, test, and deploy a neural network that predicts the rotor’s electrical position from voltage and current measurements.
Walk through the complete workflow using a Motor Control Blockset™ reference example:
• Generate training data through field-oriented control simulation.
• Create and train the neural network with Deep Learning Toolbox™.
• Validate the trained network using simulation.
• Generate code and deploy the trained virtual sensor to target hardware.
This demonstration is for motor control engineers seeking AI-based alternatives to traditional sensors, offering a scalable and cost-effective approach to rotor position estimation.
See example: bit.ly/3VQ3IOV
Motor Control Workflow with Single Deployment on Microchip MCU: bit.ly/48bNRSg
What Is Sensorless Brushless Motor Control? bit.ly/3IkiwCr
Optimal Torque Control Workflow for PMSM: bit.ly/41PB5Fc
Detect Unbalanced Motor by Using Neural Network: bit.ly/48aJcjp
Chapters:
00:00 Introduction
00:26 Quick Look: Virtual Sensor Demo
00:59 Sensorless Control for PMSM
02:01 AI-based Position Estimation Workflow
03:16 Example Walkthrough :Generate Data
07:50 Train & Test Neural Network
10:21 Embedded AI: Code Gen & Deploy
11:54 Hardware Demonstration
16:37 Conclusion
--------------------------------------------------------------------------------------------------------
Get a free product trial: goo.gl/ZHFb5u
Learn more about MATLAB: goo.gl/8QV7ZZ
Learn more about Simulink: goo.gl/nqnbLe
See what's new in MATLAB and Simulink: goo.gl/pgGtod
© 2025 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc.
See mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
