Constrained optimal input signal design for data-centric estimation methods

Sunil Deshpande, Daniel Rivera

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


This technical note examines the design of constrained input signals for data-centric estimation methods which systematically generate a local function approximation from a database of regressors at a current operating point. The proposed method addresses the optimal distribution of regressor vectors under constraints for a linear time-invariant (LTI) system. The resulting nonconvex optimization problems are solved using semidefinite relaxation methods. Numerical examples illustrate the benefits and usefulness of the proposed input signal design formulations.

Original languageEnglish (US)
Article number6882811
Pages (from-to)2990-2995
Number of pages6
JournalIEEE Transactions on Automatic Control
Issue number11
StatePublished - Nov 1 2014


  • Data-centric estimation
  • input signal design
  • semidefinite relaxation

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering


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