I am a postdoctoral researcher at the Department of Electrical and Systems Engineering, University of Pennsylvania.
My research interests include data-driven based identification and control, nonparametric systems, and formal methods for safe learning.
In modern control applications such as soft robotics or human-robot-interaction, the control design and modelling process becomes very time-consuming or even infeasible due to the complexity of the systems. A solution to overcome these issues is provided by data-driven models which require only a minimum of prior knowledge.
However, a general problem of data-driven models is the estimation of the model accuracy and a guarantee for the stability of the control loop. My research focuses on Gaussian Process (GP) models since they provide not only a mean prediction, but also a variance as uncertainty measure of the model. I derive GP based control laws which guarantee the stability of the closed loop, analyze the control properties of GP models and provide guidelines for kernel selection.
Thomas Beckers is a postdoctoral researcher at the Department of Electrical and Systems Engineering, University of Pennsylvania. In 2020, he successfully defended his PhD thesis at the Technical University of Munich (TUM), Germany. He received the B.Sc. and M.Sc. degree in electrical engineering in 2010 and 2013, respectively, from the Technical University of Braunschweig, Germany. In 2018, he was a visiting researcher at the University of California, Berkeley. His research interests include data-driven based identification and control, nonparametric systems, and formal methods for safe learning.