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Multi-agent source seeking via discrete-time extremum seeking control

Khong, Sei Zhen LU ; Tan, Ying; Manzie, Chris and Nesic, Dragan (2014) In Automatica 50(9). p.2312-2320
Abstract
Recent developments in extremum seeking theory have established a general framework for the methodology, although the specific implementations, particularly in the context of multi-agent systems, have not been demonstrated. In this work, a group of sensor-enabled vehicles is used in the context of the extremum seeking problem using both local and global optimisation algorithms to locate the extremum of an unknown scalar field distribution. For the former, the extremum seeker exploits estimates of gradients of the field from local dithering sensor measurements collected by the mobile agents. It is assumed that a distributed coordination which ensures uniform asymptotic stability with respect to a prescribed formation of the agents is... (More)
Recent developments in extremum seeking theory have established a general framework for the methodology, although the specific implementations, particularly in the context of multi-agent systems, have not been demonstrated. In this work, a group of sensor-enabled vehicles is used in the context of the extremum seeking problem using both local and global optimisation algorithms to locate the extremum of an unknown scalar field distribution. For the former, the extremum seeker exploits estimates of gradients of the field from local dithering sensor measurements collected by the mobile agents. It is assumed that a distributed coordination which ensures uniform asymptotic stability with respect to a prescribed formation of the agents is employed. An inherent advantage of the frameworks is that a broad range of nonlinear programming algorithms can be combined with a wide class of cooperative control laws to perform extreme source seeking. Semi-global practical asymptotically stable convergence to local extrema is established in the presence of field sampling noise. Subsequently, global extremum seeking with multiple agents is investigated and shown to give rise to robust practical convergence whose speed can be improved via computational parallelism. Nonconvex field distributions with local extrema can be accommodated within this global framework. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Automatica
volume
50
issue
9
pages
2312 - 2320
publisher
Pergamon
external identifiers
  • wos:000342479400010
  • scopus:84908557006
ISSN
0005-1098
DOI
10.1016/j.automatica.2014.06.009
language
English
LU publication?
yes
id
22e4728a-a5e9-437a-8caf-b143c9d15603 (old id 4739088)
date added to LUP
2014-11-09 18:23:56
date last changed
2017-10-22 04:28:56
@article{22e4728a-a5e9-437a-8caf-b143c9d15603,
  abstract     = {Recent developments in extremum seeking theory have established a general framework for the methodology, although the specific implementations, particularly in the context of multi-agent systems, have not been demonstrated. In this work, a group of sensor-enabled vehicles is used in the context of the extremum seeking problem using both local and global optimisation algorithms to locate the extremum of an unknown scalar field distribution. For the former, the extremum seeker exploits estimates of gradients of the field from local dithering sensor measurements collected by the mobile agents. It is assumed that a distributed coordination which ensures uniform asymptotic stability with respect to a prescribed formation of the agents is employed. An inherent advantage of the frameworks is that a broad range of nonlinear programming algorithms can be combined with a wide class of cooperative control laws to perform extreme source seeking. Semi-global practical asymptotically stable convergence to local extrema is established in the presence of field sampling noise. Subsequently, global extremum seeking with multiple agents is investigated and shown to give rise to robust practical convergence whose speed can be improved via computational parallelism. Nonconvex field distributions with local extrema can be accommodated within this global framework.},
  author       = {Khong, Sei Zhen and Tan, Ying and Manzie, Chris and Nesic, Dragan},
  issn         = {0005-1098},
  language     = {eng},
  number       = {9},
  pages        = {2312--2320},
  publisher    = {Pergamon},
  series       = {Automatica},
  title        = {Multi-agent source seeking via discrete-time extremum seeking control},
  url          = {http://dx.doi.org/10.1016/j.automatica.2014.06.009},
  volume       = {50},
  year         = {2014},
}