Distributed Optimization for Control

Research output: Contribution to journalReview articlepeer-review

63 Scopus citations

Abstract

Advances in wired and wireless technology have necessitated the development of theory, models, and tools to cope with the new challenges posed by large-scale control and optimization problems over networks. The classical optimization methodology works under the premise that all problem data are available to a central entity (a computing agent or node). However, this premise does not apply to large networked systems, where each agent (node) in the network typically has access only to its private local information and has only a local view of the network structure. This review surveys the development of such distributed computational models for time-varying networks. To emphasize the role of the network structure in these approaches, we focus on a simple direct primal (sub)gradient method, but we also provide an overview of other distributed methods for optimization in networks. Applications of the distributed optimization framework to the control of power systems, least squares solutions to linear equations, and model predictive control are also presented.

Original languageEnglish (US)
Pages (from-to)77-103
Number of pages27
JournalAnnual Review of Control, Robotics, and Autonomous Systems
Volume1
DOIs
StatePublished - May 28 2018
Externally publishedYes

Keywords

  • Agent networks
  • Distributed optimization
  • Multi-agent systems

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Control and Systems Engineering
  • Engineering (miscellaneous)

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