47 citations · 71 across the 14 of their papers we have counts for
3 papers · 1 filter
Deep Model Predictive Variable Impedance Control
Akhil S Anand, Fares J. Abu-Dakka, Jan Tommy Gravdahl
The capability to adapt compliance by varying muscle stiffness is crucial for dexterous manipulation skills in humans. Incorporating compliance in robot motor control is crucial to…
A novel corrective-source term approach to modeling unknown physics in aluminum extraction process
Haakon Robinson, Erlend Lundby, Adil Rasheed +1
With the ever-increasing availability of data, there has been an explosion of interest in applying modern machine learning methods to fields such as modeling and control. However,…
Sparse deep neural networks for modeling aluminum electrolysis dynamics
Erlend Torje Berg Lundby, Adil Rasheed, Ivar Johan Halvorsen +1
Deep neural networks have become very popular in modeling complex nonlinear processes due to their extraordinary ability to fit arbitrary nonlinear functions from data with minimal…