AI-based Sensorless E‑drive Control Master Thesis

About the role

Electric drives are at the heart of modern mechatronics, and the industry is rapidly moving toward sensorless control to estimate rotor positions using software rather than physical sensors. This thesis explores how Artificial Intelligence and Neural Networks can revolutionize traditional control architectures.

Key responsibilities

• Gain a solid understanding of physical models of electric drives, including machines and inverters, and the associated simulation environment.
• Analyze state‑of‑the‑art machine‑learning and neural‑network approaches applied to sensorless control of electric drives.
• Develop novel AI‑based control architectures, investigating both modular and end‑to‑end neural‑network designs.
• Implement, test, and validate the control algorithms using high‑fidelity electric‑drive simulation models.
• Document the methodology, analyse results, and present findings to the development team.

Required profile

• Enrolled in a Master program in Cybernetics, Engineering, Mathematics, Computer Science or a comparable field with good grades.
• Profound knowledge of control engineering and machine learning.
• Experience with Python or MATLAB.
• Ability to analyse complex problems, structure tasks systematically and work autonomously.
• Very good command of English.

Required skills

• Python
• MATLAB
• Control engineering
• Machine learning
• Neural networks