TY - GEN
T1 - Neuron model interpretation of a cyclic motion control concept
AU - Lakatos, Dominic
AU - Albu-Schäffer, Alin
PY - 2014/9/30
Y1 - 2014/9/30
N2 - Elastic properties of muscles and tendons are assumed to play a central role for the energy efficiency and robustness of locomotion in biological systems. Yet, the way in which the nervous system controls highly nonlinear body dynamics to produce stable periodic motions is far from being well understood. On the basis of a simple but very effective control law, which we developed and verified for variable impedance robots, we propose a controller model, which might be a very plausible hypothesis also for biological systems. The original robot controller has a bang-bang action triggered by the generalized force acting along a coordinate corresponding to the principal oscillation mode of the system. This coordinate is computed in a model-free, adaptive manner. It turns out that the control law can be easily realized with a neural network, whose weights are adapted according to the Hebbian learning rule. If this hypothesis is confirmed, cyclic body motions can be very easily and robustly implemented, with a surprisingly small number of neurons.
AB - Elastic properties of muscles and tendons are assumed to play a central role for the energy efficiency and robustness of locomotion in biological systems. Yet, the way in which the nervous system controls highly nonlinear body dynamics to produce stable periodic motions is far from being well understood. On the basis of a simple but very effective control law, which we developed and verified for variable impedance robots, we propose a controller model, which might be a very plausible hypothesis also for biological systems. The original robot controller has a bang-bang action triggered by the generalized force acting along a coordinate corresponding to the principal oscillation mode of the system. This coordinate is computed in a model-free, adaptive manner. It turns out that the control law can be easily realized with a neural network, whose weights are adapted according to the Hebbian learning rule. If this hypothesis is confirmed, cyclic body motions can be very easily and robustly implemented, with a surprisingly small number of neurons.
UR - https://www.scopus.com/pages/publications/84918557141
U2 - 10.1109/biorob.2014.6913896
DO - 10.1109/biorob.2014.6913896
M3 - Conference contribution
AN - SCOPUS:84918557141
T3 - Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics
SP - 905
EP - 910
BT - "2014 5th IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics, BioRob 2014
A2 - Carloni, Raffaella
A2 - Masia, Lorenzo
A2 - Sabater-Navarro, Jose Maria
A2 - Ackermann, Marko
A2 - Agrawal, Sunil
A2 - Ajoudani, Arash
A2 - Artemiadis, Panagiotis
A2 - Bianchi, Matteo
A2 - Lanari Bo, Antonio Padilha
A2 - Casadio, Maura
A2 - Cleary, Kevin
A2 - Deshpande, Ashish
A2 - Formica, Domenico
A2 - Fumagalli, Matteo
A2 - Garcia-Aracil, Nicolas
A2 - Godfrey, Sasha Blue
A2 - Khalil, Islam S.M.
A2 - Lambercy, Olivier
A2 - Loureiro, Rui C. V.
A2 - Mattos, Leonardo
A2 - Munoz, Victor
A2 - Park, Hyung-Soon
A2 - Rodriguez Cheu, Luis Eduardo
A2 - Saltaren, Roque
A2 - Siqueira, Adriano A. G.
A2 - Squeri, Valentina
A2 - Stienen, Arno H.A.
A2 - Tsagarakis, Nikolaos
A2 - Van der Kooij, Herman
A2 - Vanderborght, Bram
A2 - Vitiello, Nicola
A2 - Zariffa, Jose
A2 - Zollo, Loredana
PB - IEEE Computer Society
T2 - 5th IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics, BioRob 2014
Y2 - 12 August 2014 through 15 August 2014
ER -