A unified input-output approach for networked control problems with decentralized and selfish optimality

Petros G. Voulgaris, Nicola Elia

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In this paper, we take an input-output approach to enhance the study of cooperative multiagent optimization problems that admit decentralized and selfish solutions, hence eliminating the need for an interagent communication network. The framework under investigation is a set of n independent agents coupled only through an overall cost that penalizes the divergence of each agent from the average collective behavior. In the case of identical agents, or more generally agents with identical essential input-output dynamics, we show that optimal decentralized and selfish solutions are possible in a variety of standard input-output cost criteria. These include the cases of ℓ1, ℓ2, ℓ∞ induced, and H2 norms for any finite n. Moreover, if the cost includes non-deviation from average variables, the above results hold true as well for ℓ1, ℓ2, ℓ∞ induced norms and any n, while they hold true for the normalized, per-agent square H2 norm, cost as n→∞. We also consider the case of nonidentical agent dynamics and prove that similar results hold asymptotically as n→∞ in the case of ℓ2 induced norms (i.e., H∞) under a growth assumption on the H∞ norm of the essential dynamics of the collective.

Original languageEnglish (US)
Title of host publication2018 Annual American Control Conference, ACC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Print)9781538654286
StatePublished - Aug 9 2018
Externally publishedYes
Event2018 Annual American Control Conference, ACC 2018 - Milwauke, United States
Duration: Jun 27 2018Jun 29 2018

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619


Other2018 Annual American Control Conference, ACC 2018
Country/TerritoryUnited States

Bibliographical note

Funding Information:
This work was supported in part by the National Science Foundation under NSF Awards CMMI-1663460, ECCS-1739732, NSF ECCS 10-27437 and CCF 1320643, and AFOSR under Awards AF FA 9550-12-1-0193 and FA 9550-15-1-0119.

Publisher Copyright:
© 2018 AACC.


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