Distributed multi-task formation control under parametric communication uncertainties.
Distributed multi-task formation control under parametric communication uncertainties.
CDC
@inproceedings{DBLP:conf/cdc/HanP17,
author = {Dongkun Han and
Dimitra Panagou},
title = {Distributed multi-task formation control under parametric communication
uncertainties},
booktitle = {56th {IEEE} Annual Conference on Decision and Control, {CDC} 2017,
Melbourne, Australia, December 12-15, 2017},
pages = {405--410},
publisher = {{IEEE}},
year = {2017},
url = {https://doi.org/10.1109/CDC.2017.8263698},
doi = {10.1109/CDC.2017.8263698},
timestamp = {Fri, 04 Mar 2022 13:29:55 +0100},
biburl = {https://dblp.org/rec/conf/cdc/HanP17.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Abstract
Formation control is a key problem in the coordination of multiple agents. It arises new challenges to traditional formation control strategy when the communication among agents is affected by uncertainties. This paper considers the robust multi-task formation control problem of multiple nonpoint agents whose communications are disturbed by uncertain parameters. The control objectives include 1. achieving the desired configuration; 2. avoiding collisions; 3. preserving the connectedness of uncertain topology. To achieve these objectives, first, a condition of Linear Matrix Inequalities (LMIs) is proposed for checking the connectedness of uncertain topologies. Then, by preserving the initial topological connectedness, a gradient-based distributed controller is designed via Lyapunov-like barrier functions. Two numerical examples illustrate the effectiveness of the proposed method.
Authors
Bib
@inproceedings{DBLP:conf/cdc/HanP17, author = {Dongkun Han and Dimitra Panagou}, title = {Distributed multi-task formation control under parametric communication uncertainties}, booktitle = {56th {IEEE} Annual Conference on Decision and Control, {CDC} 2017, Melbourne, Australia, December 12-15, 2017}, pages = {405--410}, publisher = {{IEEE}}, year = {2017}, url = {https://doi.org/10.1109/CDC.2017.8263698}, doi = {10.1109/CDC.2017.8263698}, timestamp = {Fri, 04 Mar 2022 13:29:55 +0100}, biburl = {https://dblp.org/rec/conf/cdc/HanP17.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} }