@article{KetelhutKolditzGoelletal.2019,
  author    = {Ketelhut, Maike and Kolditz, Melanie and G{\"o}ll, Fabian and Braunstein, Bjoern and Albracht, Kirsten and Abel, Dirk},
  title     = {Admittance control of an industrial robot during resistance training},
  series = {IFAC-PapersOnLine},
  volume    = {52},
  journal   = {IFAC-PapersOnLine},
  number    = {19},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {2405-8963},
  doi       = {10.1016/j.ifacol.2019.12.102},
  pages     = {223 -- 228},
  year      = {2019},
  abstract  = {Neuromuscular strength training of the leg extensor muscles plays an important role in the rehabilitation and prevention of age and wealth related diseases. In this paper, we focus on the design and implementation of a Cartesian admittance control scheme for isotonic training, i.e. leg extension and flexion against a predefined weight. For preliminary testing and validation of the designed algorithm an experimental research and development platform consisting of an industrial robot and a force plate mounted at its end-effector has been used. Linear, diagonal and arbitrary two-dimensional motion trajectories with different weights for the leg extension and flexion part are applied. The proposed algorithm is easily adaptable to trajectories consisting of arbitrary six-dimensional poses and allows the implementation of individualized trajectories.},
  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}
}