Master Thesis on Data-Based Modelling of Electric Drives for Reinforcement Learning-Based Contr[...]

Robert Bosch Group; Malmsheim; Vor Ort; EUR 40.000 - 60.000; Vollzeit

Zusammenfassung

A multinational engineering firm in Baden-Württemberg is offering a Master Thesis on Data-Based Modelling of Electric Drives for Reinforcement Learning-Based Controller Design. The successful candidate will develop sophisticated models and control structures, conduct literature review, and generate training data. The position requires a master's level education in relevant fields and a good command of English. This role offers an opportunity for hands-on research in a dynamic environment.

Qualifikationen

• Profound knowledge of machine learning and control theory.
• Experience in Matlab/Simulink and Python.
• Knowledge of electrical machines is a plus.
• Ability to work autonomously and systematically.

Aufgaben

• Develop innovative electric drive model for direct switching controller design.
• Conduct literature review on data-based modelling and control.
• Generate training data capturing switching behavior.
• Document your work comprehensively.

Kenntnisse

Machine learning

Control theory

Matlab/Simulink

Python

Analytical thinking

English proficiency

Ausbildung

Master studies in Cybernetics, Computer Science, Engineering, Mathematics

Tools

Deep Learning frameworks

Jobbeschreibung

Overview

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

At Bosch, we shape the future by inventing high-quality technologies and services that spark enthusiasm and enrich people’s lives. Our promise to our associates is rock-solid: we grow together, we enjoy our work, and we inspire each other.

The Robert Bosch GmbH is looking forward to your application!

Responsibilities

• Full-time
• 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.

Qualifications

• 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

Start and Duration

Start: according to prior agreement

Duration: 6 months

Requirements

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

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)
[phone]