AI summary: Master's student develops AI-based neural network control algorithms for sensorless electric drive systems using simulation and machine learning techniques.
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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. The goal is to investigate and design advanced neural network concepts that learn control and estimation tasks. By mapping system measurements to control states, this research aims to pave the way for the next generation of intelligent, software-defined electric drive control.
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.
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Need further information about the job?
Felix Berkel (Functional Department)
+49 711 811 92301
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