An ADMM algorithm for optimal sensor and actuator selection

Neil K. Dhingra, Mihailo R. Jovanovic, Zhi Quan Luo

Research output: Contribution to journalConference articlepeer-review

55 Scopus citations

Abstract

We consider the problem of the optimal selection of a subset of available sensors or actuators in large-scale dynamical systems. By replacing a combinatorial penalty on the number of sensors or actuators with a convex sparsity-promoting term, we cast this problem as a semidefinite program. The solution of the resulting convex optimization problem is used to select sensors (actuators) in order to gracefully degrade performance relative to the optimal Kalman filter (Linear Quadratic Regulator) that uses all available sensing (actuating) capabilities. We employ the alternating direction method of multipliers to develop a customized algorithm that is well-suited for large-scale problems. Our algorithm scales better than standard SDP solvers with respect to both the state dimension and the number of available sensors or actuators.

Original languageEnglish (US)
Article number7040017
Pages (from-to)4039-4044
Number of pages6
JournalProceedings of the IEEE Conference on Decision and Control
Volume2015-February
Issue numberFebruary
DOIs
StatePublished - Jan 1 2014
Event2014 53rd IEEE Annual Conference on Decision and Control, CDC 2014 - Los Angeles, United States
Duration: Dec 15 2014Dec 17 2014

Keywords

  • Actuator and sensor selection
  • alternating direction method of multipliers
  • convex optimization
  • semidefinite programming
  • sparsity-promoting estimation and control

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