Mathias Tantau
Papers
2
Total Citations
7
H-Index
2
About
Mathias Tantau is a researcher focused on advancing the modeling and identification of complex industrial drive trains and robotic systems. His work centers on developing efficient methods for structure and parameter identification, particularly for systems with hard non-linearities, such as those found in stacker cranes, robots, and linear gantries. Tantau’s major contributions include pioneering the use of degenerate genetic programming to derive bright-grey box models, a technique that streamlines the traditionally time-consuming process of control design for electric drives. He has also developed sensitivity-based model reduction approaches for in-process identification of industrial robot inverse dynamics, addressing the critical challenge of parameter excitation under real-world process constraints. While his most-cited works—such as his 2019 paper on process models (5 citations) and his 2020 work on robot dynamics (2 citations)—are early in their citation lifecycle, they represent foundational steps toward more practical, automated commissioning of advanced manufacturing systems. Tantau’s research is particularly notable for bridging the gap between theoretical model identification and industrial application, offering solutions that respect the limitations of real production environments.
Research Focus
Key Achievements
Top Papers
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- 2