Bridging Theory and Practice: TalTech’s Innovative Electric Drives Course with Simulink and TI Hardware
Today’s guest bloggers are Dr. Mahmoud Ibrahim and Prof. Anton Rassõlkin, faculty members at Tallinn University of Technology (TalTech), who developed a new course dedicated to Electric Drives at the Department of Electrical Power Engineering and Mechatronics. Over to you…
Introduction: from simulation to action
At Tallinn University of Technology (TalTech), Estonia, students are doing more than just learning about motor control, they’re implementing it in real time. Dr. Mahmoud Ibrahim and Prof. Anton Rassõlkin developed a new course dedicated to Electric Drives at the Department of Electrical Power Engineering and Mechatronics.
In the course blending theory with practice, master’s students use Simulink and Texas Instruments (TI) hardware to deploy, test, and fine-tune control algorithms on actual motors. This innovative approach is part of a broader initiative supported by the Recovery and Resilience Facility (RRF) under the project “Skills Reform for the Digital Transformation of Businesses”. The initiative aims to enhance engineering education by equipping future professionals with industry-relevant digital skills through practical learning experiences.

Figure 1. Supervising the student in the course.
Bridging Theory and Industry
Electric drives are critical in applications ranging from electric vehicles (EVs), drones, robotics, and industrial automation. While traditional coursework provides solid theory and limited practice, many students graduate without ever deploying a control algorithm to real hardware. Our goal was to design a portable, scalable lab kit and a curriculum that would make motor control hands-on and approachable for master’s-level students.
Building the Platform
We created a custom lab kit, as shown in Figure 2, that includes:
- TI Delfino F28379D Board
- Two BOOSTXL-DRV8305EVM Motor Drivers
- Two Teknic M-2310P-LN-04K PMSM in dyno setup (with encoders and Hall sensors)
All components were enclosed in a user-friendly, compact setup that students could easily operate during sessions.

Figure 2. Electric drives kit
Course Structure
We’ve carefully designed each session of our course not just as standalone modules, but as building blocks reflecting the realities of today’s industry:
Session 1: Kickstarting Control with Rapid Control Prototyping (RCP)
Starting simple yet powerful, students begin with scalar (open-loop) control, fundamental for hands-on familiarity. This initial experience lets them quickly gain confidence by seeing their algorithms spin a real motor for the first time.
Session 2: Precision Matters – Sensor Calibration & Motor Parameter Estimation
Before diving deeper, students learn essential preliminary skills—accurately estimating parameters (resistance, inductance, back EMF) and calibrating sensors (encoders, Hall sensors). This critical step ensures reliable and efficient motor control later on.
Session 3: Mastering Field-Oriented Control (FOC) for Mobility
With applications ranging from electric vehicles to drones, FOC represents the gold standard for dynamic, high-performance motor control. Students experiment with both sensor-based and sensorless configurations, tuning their systems for optimal precision and responsiveness.
Session 4: Direct Torque Control (DTC) – Powering Industry
Students explore the robust simplicity and rapid response of DTC, widely used in industrial machinery and heavy-duty drives. Through direct comparisons with FOC, they grasp real-world trade-offs, equipping them to choose the right tool for the right task.
Session 5: The Dyno Challenge – Putting It All Together
In the final challenge, two motors work in tandem—one driving, one loading—to simulate real operating conditions. Students test dynamic response, stability, and efficiency under realistic conditions, experiencing firsthand the practical implications of their control strategies.
Implementation
The implementation of the lab works followed a structured six-step workflow, guiding students from theoretical concepts to hands-on real-time testing and analysis. This workflow, illustrated in the accompanying figure below, ensured a comprehensive learning experience by combining prebuilt Simulink models, included in the Motor Control Blockset library, with practical hardware deployment and analysis.

Importing Prebuilt Simulation Model:
Students began by importing prebuilt Simulink models specifically designed to simplify the learning process. These models served as a foundation, providing a structured framework for understanding motor control systems without the need to build models from scratch.
Exploring Different Model Parameters and Blocks:
Once the prebuilt models were imported, students explored their structure in detail. They analyzed the individual blocks, identified control algorithms, and examined adjustable parameters. This step emphasized the importance of understanding how various components interact within the control system, encouraging students to visualize additional data and modify parameters to suit different scenarios.
Model Simulation and Validation:
The next step involved simulating the models in a controlled environment. Students validated the functionality of the control algorithms by observing system behavior through graphical outputs, such as speed, torque, and current curves. This phase allowed them to refine their understanding and ensure the model’s correctness before proceeding to hardware deployment.
Deployment to the Hardware Kit (Motor Control Setup):
After validation, students deployed the control algorithms to the hardware kit. This step required configuring the Simulink model for hardware operation and establishing secure connections between the hardware elements.
Testing the motor in real time
Students love seeing their models come to life. Figures 3-4 show sample examples from students’ reports from simulation and real-time motor operation under sensor less FOC.

Figure 3. Motor speed performance simulation under sensor less FOC obtained from simulation and real-time testing

Figure 4. Motor direct axis current under sensor less FOC obtained from simulation and real-time testing.
Performance analysis – What Students Are Saying
Feedback was overwhelmingly positive. Students appreciated the structure, clarity, and how closely the tasks reflected industry practices. Figure 5. provides an anonymous survey of students’ satisfaction with the course.
“This course helped me understand how control systems actually behave—not just in simulation, but on real motors.”

Figure 5. Students’ satisfaction score.
Looking Ahead
We plan to enhance the course with:
- AI-based motor control
- Expanded applications (e.g., renewables and robotics)
- More motor types and sensor options
- Remote and hybrid lab accessibility
We’re continuously evolving to match the pace of technology, and Simulink is at the heart of that journey.
What’s most exciting is how this initiative is already influencing others. As part of our dissemination efforts, we visited several Estonian universities, including Tallinn University of Applied Sciences (TKTK) and the Estonian University of Life Sciences (EMÜ), where we demonstrated the course outcomes and the developed control setup. The response was incredibly positive, these institutions are now exploring how to integrate similar hands-on, Simulink-based approaches into their teaching process. This momentum marks the beginning of a new era in electric drives education across Estonia, one grounded in practical application, student engagement, and industry-aligned learning. Figure 6 below shows pictures from the dissemination visits to other Estonian universities.

(a)

(b)
Figure 6. (a) Test kit presentation at Tallinn University of Applied Sciences (TKTK) and (b) Estonian University of Life Sciences (EMÜ) in Tartu
- Category:
- Education,
- Simulink
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