Optimal input signal design for data-centric estimation methods

Sunil Deshpande, Daniel Rivera

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

6 Scopus citations


Data-centric estimation methods such as Model-on-Demand and Direct Weight Optimization form attractive techniques for estimating unknown functions from noisy data. These methods rely on generating a local function approximation from a database of regressors at the current operating point with the process repeated at each new operating point. This paper examines the design of optimal input signals formulated to produce informative data to be used by local modeling procedures. The proposed method specifically addresses the distribution of the regressor vectors. The design is examined for a linear time-invariant system under amplitude constraints on the input. The resulting optimization problem is solved using semidefinite relaxation methods. Numerical examples show the benefits in comparison to a classical PRBS input design.

Original languageEnglish (US)
Title of host publication2013 American Control Conference, ACC 2013
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Print)9781479901777
StatePublished - 2013
Event2013 1st American Control Conference, ACC 2013 - Washington, DC, United States
Duration: Jun 17 2013Jun 19 2013

Publication series

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


Other2013 1st American Control Conference, ACC 2013
Country/TerritoryUnited States
CityWashington, DC

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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