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Consensus control for linear systems with optimal energy costPrimeFaces.cw("AccordionPanel","widget_formSmash_some",{id:"formSmash:some",widgetVar:"widget_formSmash_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_all",{id:"formSmash:all",widgetVar:"widget_formSmash_all",multiple:true});
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2018 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 93, p. 83-91Article in journal (Refereed) Published
##### Abstract [en]

##### Place, publisher, year, edition, pages

Elsevier, 2018. Vol. 93, p. 83-91
##### Keywords [en]

Consensus control, Distributed optimization, Multi-agent systems, Optimal control, Semi-definite programming
##### National Category

Control Engineering
##### Identifiers

URN: urn:nbn:se:kth:diva-227534DOI: 10.1016/j.automatica.2018.03.044ISI: 000436916200010Scopus ID: 2-s2.0-85044478071OAI: oai:DiVA.org:kth-227534DiVA, id: diva2:1208871
#####

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##### Note

##### In thesis

In this paper, we design an optimal energy cost controller for linear systems asymptotic consensus given the topology of the graph. The controller depends only on relative information of the agents. Since finding the control gain for such controller is hard, we focus on finding an optimal controller among a classical family of controllers which is based on Algebraic Riccati Equation (ARE) and guarantees asymptotic consensus. Through analysis, we find that the energy cost is bounded by an interval and hence we minimize the upper bound. In order to do that, there are two classes of variables that need to be optimized: the control gain and the edge weights of the graph and are hence designed from two perspectives. A suboptimal control gain is obtained by choosing Q=0 in the ARE. Negative edge weights are allowed, and the problem is formulated as a Semi-definite Programming (SDP) problem. Having negative edge weights means that “competitions” between the agents are allowed. The motivation behind this setting is to have a better system performance. We provide a different proof compared to Thunberg and Hu (2016) from the angle of optimization and show that the lowest control energy cost is reached when the graph is complete and with equal edge weights. Furthermore, two sufficient conditions for the existence of negative optimal edge weights realization are given. In addition, we provide a distributed way of solving the SDP problem when the graph topology is regular.

QC 20180521

Available from: 2018-05-21 Created: 2018-05-21 Last updated: 2019-01-21Bibliographically approved1. Optimizing Networked Systems and Inverse Optimal Control$(function(){PrimeFaces.cw("OverlayPanel","overlay1280925",{id:"formSmash:j_idt720:0:j_idt724",widgetVar:"overlay1280925",target:"formSmash:j_idt720:0:parentLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

doi
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