Master Thesis Robust Identification of Compositional Electrical Drive Models - Identifiability, Sensitivity & Excitation Analysis

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During your thesis, you delve into physics-based models of electric drives and our Python component library while simultaneously reviewing current literature on robust system identification and the design of experiments. Furthermore, you develop innovative robustness diagnostics for our existing identification pipeline - considering parameter identifiability, sensitivity, and convergence behavior - and implement as well as quantify appropriate countermeasures.

In Addition, you analyze the excitation content of given datasets, evaluate relevant criteria, and link these insights to the achievable identification quality. You comprehensively evaluate the approach you have developed using a practical benchmark use case.

Moreover, you document as well as present your research findings clearly and understandably. Lastly, you extend the approach to address part