TY - GEN
T1 - Adaptive Neural Network Dynamic Surface Control
T2 - An Evaluation on the Musculoskeletal Robot Anthrob
AU - Jaentsch, Michael
AU - Wittmeier, Steffen
AU - Dalamagkidis, Konstantinos
AU - Herrmann, Guido
AU - Knoll, Alois
PY - 2015/8
Y1 - 2015/8
N2 - The soft robotics approach is widely considered to enable robots in the near future to leave their cages and move freely in our modern homes and manufacturing sites. Musculoskeletal robots are such soft robots which feature passively compliant actuation, while leveraging the advantages of tendon-driven systems. Even though these robots have been intensively researched within the last decade, high-performance feedback control laws have only very recently been developed. In [1], a controller was developed utilizing Dynamic Surface Control (DSC), an extension to backstepping, with an adaptive neural network compensator for joint as well as muscle friction. We compare these novel control strategies to Computed Force Control (CFC), an existing technique from the field of tendon-driven control, yielding highly improved trajectory tracking. The musculoskeletal robot Anthrob [2] serves as a benchmark.
AB - The soft robotics approach is widely considered to enable robots in the near future to leave their cages and move freely in our modern homes and manufacturing sites. Musculoskeletal robots are such soft robots which feature passively compliant actuation, while leveraging the advantages of tendon-driven systems. Even though these robots have been intensively researched within the last decade, high-performance feedback control laws have only very recently been developed. In [1], a controller was developed utilizing Dynamic Surface Control (DSC), an extension to backstepping, with an adaptive neural network compensator for joint as well as muscle friction. We compare these novel control strategies to Computed Force Control (CFC), an existing technique from the field of tendon-driven control, yielding highly improved trajectory tracking. The musculoskeletal robot Anthrob [2] serves as a benchmark.
KW - backstepping, Compliant actuation, musculoskeletal robots, non-linear control, adaptive control
U2 - 10.1109/ICRA.2015.7139799
DO - 10.1109/ICRA.2015.7139799
M3 - Conference Contribution (Conference Proceeding)
SN - 9781479969241
SP - 4347
EP - 4352
BT - 2015 IEEE International Conference on Robotics and Automation (ICRA 2015)
PB - Institute of Electrical and Electronics Engineers (IEEE)
ER -