Concurrent learning adaptive control for linear switched systems

Gerardo De La Torre, Girish Chowdhary, Eric N. Johnson

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

12 Scopus citations

Abstract

A concurrent learning adaptive control architecture for uncertain linear switched dynamical systems is presented. Like other concurrent learning adaptive control architectures, the adaptive weight update law uses both recorded and current data concurrently for adaptation. In addition, a verifiable condition on the linear independence of the recorded data is shown to be sufficient to guarantee global exponential stability and adaptive weight convergence. Furthermore, it is shown that the recorded data eventually meets this condition after a system switch without any additional excitation from the exogenous reference input or knowledge of the switching signal if there is sufficient time in between switches. That is, after a switch, the system will be automatically excited and sufficiently rich data will be recorded. As a result, data that is irrelevant to the current subsystem will be overwritten. Thus, reference model tracking error and adaptive weight error will eventually become globally exponential stable for all switched subsystems. Numerical examples are presented to illustrate the effectiveness of the proposed architecture.

Original languageEnglish (US)
Title of host publication2013 American Control Conference, ACC 2013
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages854-859
Number of pages6
ISBN (Print)9781479901777
DOIs
StatePublished - 2013
Event2013 1st American Control Conference, ACC 2013 - Washington, DC, United States
Duration: Jun 17 2013Jun 19 2013

Publication series

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

Other

Other2013 1st American Control Conference, ACC 2013
CountryUnited States
CityWashington, DC
Period6/17/136/19/13

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

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