@conference{
	11580_71243,
	author = {Marino, Alessandro},
	title = {A Decentralized Robust Adaptive Control for Tightly Connected Networked Lagrangian Systems},
	year = {2017},
	publisher = {IEEE},
	booktitle = {56th IEEE Conference on Decision and Control},
	abstract = {In this work, a decentralized control strategy for tightly
connected networked Lagrangian systems is designed. The main characteristics
of the solution is that it allows to both control the motion of
the handled object and the squeezing wrenches arising on it and this is
achieved by resorting to a layered architecture. At the top layer, robots
exploit consensus theory to distributedly estimate the full state of the
system and the object dynamics to estimate the squeezing wrenches,
while, at the second layer, a local adaptive control law is specified in
order to both control the local contribute to the squeezing wrenches
and the local motion of the robot. The effectiveness of the solution is
proven by employing 6-DOFs serial chain manipulators mounted on a
mobile platform to perform a cooperative load transportation task.},
	keywords = {Distributed observer-based control; cooperative manipulation},
	url = {https://ieeexplore.ieee.org/document/8264347/},
	doi = {10.1109/CDC.2017.8264347},
	isbn = {978-1-5090-2872-6},	
	pages = {4656--4661}
}

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