Master Thesis on Data-Based Modelling of Electric Drives for Reinforcement Learning-Based Controller Design

Your tasks

The performance and efficiency of electric drives are fundamentally determined by their control methods and modulation schemes. While conventional approaches rely on simplified models and control structures, these limitations often restrict optimal performance in real‑world applications. Reinforcement Learning (RL) has emerged as a promising solution, offering the potential to enhance performance through more sophisticated models and control structures, e.g. direct switching control which directly manipulates the switching time instants of the inverter terminals. However, RL agents trained in simulation environments using simplified models frequently experience performance gaps when deployed in real‑world scenarios. The main objective of this thesis is the development of an innovative electric drive model suitable for a direct switching controller design using reinforcement learning.

• During your thesis you will conduct a comprehensive literature review on data-based modelling and control of electric drives.
• You will develop a concept for electric drive system excitation for generating training data capturing the switching behavior.
• Furthermore, you will elaborate an electric drive model that captures the switching behavior using physics-based and data-based modelling techniques.
• Optionally, you will train and evaluate a direct switching controller using reinforcement learning and the developed models.
• Finally, the documentation of your work also falls within your area of responsibility.

Your profile

• Education: Master studies in the field of Cybernetics, Computer Science, Engineering, Mathematics or comparable
• Experience and Knowledge: profound knowledge of machine learning and control theory; experience in Matlab/Simulink and Python, ideally in DL frameworks; knowledge of electrical machines is a plus
• Personality and Working Practice: you excel at working autonomously, systematically organizing your tasks, and applying analytical thinking to solve complex problems
• Languages: very good in English

Contact & additional information

Start: according to prior agreement
Duration: 6 months

Requirement for this thesis is the enrollment at university. Please attach your CV, transcript of records, examination regulations and if indicated a valid work and residence permit.

Diversity and inclusion are not just trends for us but are firmly anchored in our corporate culture. Therefore, we welcome all applications, regardless of gender, age, disability, religion, ethnic origin or sexual identity.

Need further information about the job?
Felix Berkel (Functional Department)
+49 711 811 92301

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