A generalized framework for perturbation-based derivative estimation in multivariable extremum-seeking

R. van der Weijst, T.A.C. van Keulen, F.P.T. Willems

Research output: Contribution to journalConference articlepeer-review

3 Citations (Scopus)
3 Downloads (Pure)

Abstract

In the context of model-free optimization of dynamic nonlinear multiple-input-single-output (MISO) systems using extremum-seeking (ES), accurate and fast derivative estimation of the systems steady-state performance map is essential. This paper presents a generalized derivative estimator (DE) framework for unknown MISO static maps. To this extent, the map input is perturbed with sinusoidal dither signals with different frequencies. Using the proposed framework, the derivatives can be estimated up to an arbitrary order, for maps with an arbitrary number of inputs. Conditions on the dither frequencies are provided, which optimize the DE time-scale, such that derivative estimation is as fast as possible. Simulation examples are provided to demonstrate the effectiveness of the proposed framework.

Original languageEnglish
Pages (from-to)3148-3153
Number of pages6
JournalIFAC-PapersOnLine
Volume50
Issue number1
DOIs
Publication statusPublished - 1 Jul 2017
Event20th World Congress of the International Federation of Automatic Control (IFAC 2017 World Congress) - Toulouse, France
Duration: 9 Jul 201714 Jul 2017
Conference number: 20
https://www.ifac2017.org/

Keywords

  • adaptive control
  • autotuning
  • data-based control
  • Extremum-seeking
  • multivariable systems
  • nonlinear systems

Fingerprint Dive into the research topics of 'A generalized framework for perturbation-based derivative estimation in multivariable extremum-seeking'. Together they form a unique fingerprint.

Cite this