Prof. Dr.-Ing./Univ. Tokio Martin Buss
Academic Career and Research Areas
Professor Buss researches methods in control and system theory, in particular hybrid (discrete/continuous), switching, nonlinear dynamic systems for use in mechatronics, robotics, medical technology, communication technology, telepresence systems, teleaction systems and man-machine systems.
Professor Buss studied electrical engineering at TH Darmstadt and was awarded a doctorate at the University of Tokyo (1994). In 2000, he completed his lecturer qualification at TUM and was appointed to the Chair of Control Systems at TU Berlin. He has been full professor of control technology at TUM since 2003. In 2014, he became fellow of the Institute of Electrical and Electronics Engineers (IEEE).
Awards
- Bundesverdienstkreuz am Bande (2011)
- ICMA 2009 - Toshio Fukuda Best Paper Award in Mechatronics (2009)
- Shakey - Most Innovative Video Award (2009)
- Best Journal Paper Award, at–Automatisierungstechnik (2006)
- Hugo Schuck Best Paper Award, American Control Conference (ACC) (2001)
Key Publications (all publications)
Dang, Ni; Brüdigam, Tim; Zhang, Zengjie; Liu, Fangzhou; Leibold, Marion; Buss, Martin: Distributed Stochastic Model Predictive Control for a Microscopic Interactive Traffic Model. Electronics (12(6), 1270), 2023.
AbstractLi, Cong; Wang, Yongchao; Liu, Fangzhou; Liu, Qingchen; Buss, Martin: Model-free Incremental Adaptive Dynamic Programming based Approximate Robust Optimal Regulation. International Journal of Robust and Nonlinear Control 32 (5), 2022, 2662-2682.
AbstractLiu, Tong; Buss, Martin: Model Reference Adaptive Control of Piecewise Affine Systems with State Tracking Performance Guarantee. International Journal of Robust and Nonlinear Control 32 (7), 2022, 4129-4148.
AbstractWang, Yongchao; Zhang, Zengjie; Li, Cong; Buss, Martin: Adaptive Incremental Sliding Mode Control for a Robot Manipulator. Mechatronics 82, 2022, 102717.
AbstractDu, Yingwei; Liu, Fangzhou; Qiu, Jianbin; Buss, Martin: Online Identification of Piecewise Affine Systems Using Integral Concurrent Learning. IEEE Transactions on Circuits and Systems I: Regular Papers 68 (10), 2021, 4324-4336.
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