Adaptive Iterative Learning Control of an Industrial Robot during Neuromuscular Training

Maike Ketelhut*, G.M. Brügge, Fabian Göll, Bjoern Braunstein, Kirsten Albracht, D. Abel

*Corresponding author for this work

Publication: Contribution to journalJournal articlesResearchpeer-review


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.
Original languageEnglish
Issue number2
Pages (from-to)16468-16475
Number of pages8
Publication statusPublished - 2020
EventIFAC World Congress - Berlin, Germany
Duration: 12.07.202017.07.2020
Conference number: 21