Continuous-Time Extremum Seeking with Function Measurements Disturbed by Stochastic Noise: A Synchronous Detection Approach

Cesar U. Solis, Julio B. Clempner, Alexander S. Poznyak

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

This paper suggests a novel algorithm for extremum seeking based on a stochastic continuous-time optimization approach employing a gradient descent method based on the synchronous detection technique. The problem consists on finding the minimum of a strongly convex function which is unknown but may be measured in any testing point subject to a stochastic noise perturbation. The suggested extremum seeking procedure is based on the estimated gradient obtained by the modified stochastic version of the Synchronous Detection Method. We have added a first order low-pass filter to the gradient estimator to attenuate the noise in the estimations. We prove the mean-squared convergence of the suggested extremum seeking algorithm to a zone around the minimizer. To validate the contributions of the paper we present a numerical example.

Original languageEnglish
Title of host publication2018 15th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538670323
DOIs
StatePublished - 13 Nov 2018
Event15th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2018 - Mexico City, Mexico
Duration: 5 Sep 20187 Sep 2018

Publication series

Name2018 15th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2018

Conference

Conference15th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2018
Country/TerritoryMexico
CityMexico City
Period5/09/187/09/18

Keywords

  • Extremum Seeking
  • Real-time optimization
  • Synchronous Detection Method

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