Hybrid model reference adaptive control for unmatched uncertainties

John F. Quindlen, Girish Chowdhary, Jonathan P. How

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


This paper presents a hybrid model reference adaptive control approach for systems with both matched and unmatched uncertainties. This approach extends concurrent learning adaptive control to a wider class of systems with unmatched uncertainties that lie outside the space spanned by the control input, and therefore cannot be directly suppressed with inputs. The hybrid controller breaks the problem into two parts. First, a concurrent learning identification law guarantees the estimates of the unmatched parameterization converges to the actual values in a determinable rate. While this begins, a robust reference model and controller maintain stability of the tracking and matched parameterization error. Once the unmatched estimates have converged, the system exploits this information to switch to a more aggressive controller to guarantee global asymptotic convergence of all tracking, matched, and unmatched errors to zero. Simulations of simple aircraft dynamics demonstrate this stability and convergence.

Original languageEnglish (US)
Title of host publicationACC 2015 - 2015 American Control Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781479986842
StatePublished - Jul 28 2015
Externally publishedYes
Event2015 American Control Conference, ACC 2015 - Chicago, United States
Duration: Jul 1 2015Jul 3 2015

Publication series

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


Conference2015 American Control Conference, ACC 2015
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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