Summary

Jonas Weigand studied mechanical engineering (B.Sc.) and automotive engineering (M.Sc.) at the RWTH Aachen University. In his master thesis at the Institute of Control Engineering he dealt with the "System identification of a CNC machining center with artificial neural networks. Since November 2017 he is working as a research assistant at the Department of Machine Tools and Controls.

Research Fields

Jonas Weigand deals with the further development of industrial robots into machine tools. The focus is on the combination of machine learning techniques with control theory.

Publications

Dataset and Baseline for an Industrial Robot Identification Benchmark, J. Weigand, J. Götz, J. Ulmen, M. Ruskowksi, 2022, Kluedo-Link

Input-to-state stability for system identification with continuous-time Runge–Kutta neural networks, J. Weigand, M. Deflorian, M. Ruskowski, 2021, International Journal of Control, 1-17. Link

Hybrid Data-Driven Modelling for Inverse Control of Hydraulic Excavators, J. Weigand, J. Raible, N. Zantopp, O. Demir, A. Trachte, A. Wagner, M. Ruskowski, 2021, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Link

Flatness Based Control of an Industrial Robot Joint Using Secondary Encoders, J Weigand, N Gafur, M Ruskowski, 2021, Robotics and Computer-Integrated Manufacturing 68, 102039. Link (arXiv)

Neural Adaptive Control of a Robot Joint Using Secondary Encoders, J Weigand, M Volkmann, M Ruskowski, 2019, International Conference on Robotics in Alpe-Adria Danube Region, 153-161. Link