On the design of optimal structured and sparse feedback gains via sequential convex programming

Makan Fardad, Mihailo R. Jovanović

Research output: Chapter in Book/Entry/PoemConference contribution

45 Scopus citations


We consider the problem of finding optimal feedback gains in the presence of structural constraints and/or sparsity-promoting penalty functions. Such problems are known to be difficult due to their lack of convexity. We provide an equivalent reformulation of the optimization problem such that its source of nonconvexity is isolated in one nonconvex matrix inequality of the form Y X -1. Furthermore, we preserve the feedback gain as an optimization variable in the reformulated problem. Via linearizations of the nonconvex constraint, we introduce an iterative algorithm that solves a semidefinite program at every stage and for which the nonconvex constraint is satisfied upon convergence. We elaborate on the modular nature of the proposed scheme and show that it can be used in a wide range of network control problems.

Original languageEnglish (US)
Title of host publication2014 American Control Conference, ACC 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Print)9781479932726
StatePublished - 2014
Event2014 American Control Conference, ACC 2014 - Portland, OR, United States
Duration: Jun 4 2014Jun 6 2014

Publication series

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


Other2014 American Control Conference, ACC 2014
Country/TerritoryUnited States
CityPortland, OR


  • Communication architecture
  • optimization
  • semidefinite programming
  • sequential convex programming
  • sparsity-promoting control
  • structural constraints

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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