Combining self-optimizing control and extremum seeking for online optimization with application to Vapor Compression cycles

Bryan D. Keating, Andrew Alleyne

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


The goal of online optimization is to find economizing input values that minimize the plant's steady-state operational cost. Often, the optimal value of these inputs is a function of the system's disturbances. For systems with a direct measurement of the operational cost function and multiple process outputs, there is a wealth of information that can be exploited for online optimizing feedback control. When extremum seeking control is applied to these systems, multiple potential process measurements unrelated to achieving system performance objectives present a choice of the extremum seeking controlled variable. In this paper, a Vapor Compression System moving boundary simulation model is employed to investigate the effectiveness of combining self-optimizing control with extremum seeking control. Results show that combining extremum seeking's ability to adapt to slowly varying disturbances under minimal assumptions about the system model with the transient performance guarantees provided by self-optimizing control improves optimization performance by nearly 60% relative to the case where extremum seeking directly controls an actuator input.

Original languageEnglish (US)
Title of host publication2016 American Control Conference, ACC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781467386821
StatePublished - Jul 28 2016
Event2016 American Control Conference, ACC 2016 - Boston, United States
Duration: Jul 6 2016Jul 8 2016

Publication series

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


Other2016 American Control Conference, ACC 2016
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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