TY  - CHAP
A1  - Ketelhut, Maike
A1  - Göll, Fabian
A1  - Braunstein, Bjoern
A1  - Albracht, Kirsten
A1  - Abel, Dirk
T1  - Iterative learning control of an industrial robot for neuromuscular training
T2  - 2019 IEEE Conference on Control Technology and Applications
N2  - Effective training requires high muscle forces potentially leading to training-induced injuries. Thus, continuous monitoring and controlling of the loadings applied to the musculoskeletal system along the motion trajectory is required. In this paper, a norm-optimal iterative learning control algorithm for the robot-assisted training is developed. The algorithm aims at minimizing the external knee joint moment, which is commonly used to quantify the loading of the medial compartment. To estimate the external knee joint moment, a musculoskeletal lower extremity model is implemented in OpenSim and coupled with a model of an industrial robot and a force plate mounted at its end-effector. The algorithm is tested in simulation for patients with varus, normal and valgus alignment of the knee. The results show that the algorithm is able to minimize the external knee joint moment in all three cases and converges after less than seven iterations.
KW  - Knee
KW  - Training
KW  - Load modeling
KW  - Force
KW  - Iterative learning control
Y1  - 2019
SN  - 978-1-7281-2767-5 (ePub)
SN  - 978-1-7281-2766-8 (USB)
SN  - 978-1-7281-2768-2 (PoD)
U6  - http://dx.doi.org/10.1109/CCTA.2019.8920659
N1  - 2019 IEEE Conference on Control Technology and Applications (CCTA) Hong Kong, China, August 19-21, 2019
PB  - IEEE
CY  - New York
ER  - 
TY  - CHAP
A1  - Ulmer, Jessica
A1  - Mostafa, Youssef
A1  - Wollert, Jörg
T1  - Digital Twin Academy: From Zero to Hero through individual learning experiences
T2  - Tagungsband AALE 2022 / Herausgegeben von der Hochschule für Technik, Wirtschaft und Kultur Leipzig
N2  - Digital twins are seen as one of the key technologies of Industry 4.0. Although many research groups focus on digital twins and create meaningful outputs, the technology has not yet reached a broad application in the industry. The main reasons for this imbalance are the complexity of the topic, the lack of specialists, and the unawareness of the twin opportunities. The project "Digital Twin Academy" aims to overcome these barriers by focusing on three actions: Building a digital twin community for discussion and exchange, offering multi-stage training for various knowledge levels, and implementing realworld use cases for deeper insights and guidance. In this work, we focus on creating a flexible learning platform that allows the user to select a training path adjusted to personal knowledge and needs. Therefore, a mix of basic and advanced modules is created and expanded by individual feedback options. The usage of personas supports the selection of the appropriate modules.
KW  - Digital Twins
KW  - Knowledge Transfer
KW  - Training
Y1  - 2022
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bsz:l189-qucosa2-776097
SN  - 978-3-910103-00-9
N1  - Konferenz: 18. AALE-Konferenz. Pforzheim, 09.03.-11.03.2022

This cross-border research was conducted in frame of the Interreg Euregio Meuse-Rhine project Digital Twin Academy, funded by the European Regional Development Fund of the European Union.
SP  - 1
EP  - 9
ER  -