Masterarbeit: KI-basierte Regelung der Leistungselektronik (w/m/div.)
Core
Develop next-generation closed-loop control algorithms for highly nonlinear power electronic systems (AC/DC and DC/DC converters) to improve efficiency and stability in applications like e-mobility and robotics.
Role type
Master's thesis researcher (Control Systems & Power Electronics)
Builds
Simulation models and control strategies for next-generation charger converter topologies
Domain
Power Electronics, Control Theory, Machine Learning
Deliverable
production ML models | research
Required skills
Control systems (state-space, nonlinear control, predictive control), Simulation tools (MATLAB/Simulink, Python), Power electronics (AC/DC, DC/DC converters)
Preferred skills
Machine Learning frameworks (PyTorch), Model Predictive Control (MPC), Reinforcement Learning (RL)
Technologies
MATLAB/Simulink, Python, PyTorch, PLECS
Responsibilities
Investigate and compare advanced closed-loop control methods (MPC, neural networks, RL), implement algorithms in simulation, validate and benchmark concepts under realistic conditions, analyze performance metrics (computational effort, dynamics, robustness), document and present results
Seniority
Master's student (Research Assistant)