@inproceedings{KetelhutGoellBraunsteinetal.2019,
  author    = {Ketelhut, Maike and G{\"o}ll, Fabian and Braunstein, Bjoern and Albracht, Kirsten and Abel, Dirk},
  title     = {Iterative learning control of an industrial robot for neuromuscular training},
  series = {2019 IEEE Conference on Control Technology and Applications},
  booktitle = {2019 IEEE Conference on Control Technology and Applications},
  publisher = {IEEE},
  address   = {New York},
  isbn      = {978-1-7281-2767-5 (ePub)},
  doi       = {10.1109/CCTA.2019.8920659},
  pages     = {7 Seiten},
  year      = {2019},
  abstract  = {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.},
  language  = {en}
}
@article{KetelhutBrueggeGoelletal.2020,
  author    = {Ketelhut, Maike and Br{\"u}gge, G. M. and G{\"o}ll, Fabian and Braunstein, Bjoern and Albracht, Kirsten and Abel, Dirk},
  title     = {Adaptive iterative learning control of an industrial robot during neuromuscular training},
  series = {IFAC PapersOnLine},
  volume    = {53},
  journal   = {IFAC PapersOnLine},
  number    = {2},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {2405-8963},
  doi       = {10.1016/j.ifacol.2020.12.741},
  pages     = {16468 -- 16475},
  year      = {2020},
  abstract  = {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.},
  language  = {en}
}