Global extremum seeking by Kriging with a multi-agent system

Abstract : This paper presents a method for finding the global maximum of a spatially varying field using a multi-agent system. A surrogate model of the field is determined via Kriging (Gaussian process regression) from a set of sampling measurements collected by the agents. A criterion exploiting Kriging statistical properties is introduced for selecting new sampling points that each vehicle must rally. These new points are obtained as a compromise between improvement of the estimate of the global maximum and traveling distance. A cooperative control law is proposed to move the agents to the desired sampling positions while avoiding collisions. Simulation results show the interest of the method and how it compares with a state-of-art solution.
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Pré-publication, Document de travail
Preprint 17th IFAC Symposium on System Identification, SYSID 2015. 2015
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Dernière modification le : mercredi 23 janvier 2019 - 14:39:32
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  • HAL Id : hal-01170131, version 2

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Arthur Kahn, Julien Marzat, Hélène Piet-Lahanier, Michel Kieffer. Global extremum seeking by Kriging with a multi-agent system. Preprint 17th IFAC Symposium on System Identification, SYSID 2015. 2015. 〈hal-01170131v2〉

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