TY  - JOUR
A1  - Ketelhut, Maike
A1  - Brügge, G. M.
A1  - Göll, Fabian
A1  - Braunstein, Bjoern
A1  - Albracht, Kirsten
A1  - Abel, Dirk
T1  - Adaptive iterative learning control of an industrial robot during neuromuscular training
T2  - IFAC PapersOnLine
N2  - To prevent the reduction of muscle mass and loss of strength coming along with the human aging process, regular training with e.g. a leg press is suitable. However, the risk of training-induced injuries requires the continuous monitoring and controlling of the forces applied to the musculoskeletal system as well as the velocity along the motion trajectory and the range of motion. In this paper, an adaptive norm-optimal iterative learning control algorithm to minimize the knee joint loadings during the leg extension training with an industrial robot is proposed. The response of the algorithm is tested in simulation for patients with varus, normal and valgus alignment of the knee and compared to the results of a higher-order iterative learning control algorithm, a robust iterative learning control and a recently proposed conventional norm-optimal iterative learning control algorithm. Although significant improvements in performance are made compared to the conventional norm-optimal iterative learning control algorithm with a small learning factor, for the developed approach as well as the robust iterative learning control algorithm small steady state errors occur.
KW  - Iterative learning control
KW  - Robotic rehabilitation
KW  - Adaptive control
Y1  - 2020
UR  - https://opus.bibliothek.fh-aachen.de/opus4/frontdoor/index/index/docId/10982
SN  - 2405-8963
VL  - 53
IS  - 2
SP  - 16468
EP  - 16475
PB  - Elsevier
CY  - Amsterdam
ER  -