Concurrent learning for convergence in adaptive control without persistency of excitation

Girish Chowdhary, Eric Johnson

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

141 Scopus citations

Abstract

We show that for an adaptive controller that uses recorded and instantaneous data concurrently for adaptation, a verifiable condition on linear independence of the recorded data is sufficient to guarantee exponential tracking error and parameter error convergence. This condition is found to be less restrictive and easier to monitor than a condition on persistently exciting exogenous input signal required by traditional adaptive laws that use only instantaneous data for adaptation.

Original languageEnglish (US)
Title of host publication2010 49th IEEE Conference on Decision and Control, CDC 2010
Pages3674-3679
Number of pages6
DOIs
StatePublished - Dec 1 2010
Event2010 49th IEEE Conference on Decision and Control, CDC 2010 - Atlanta, GA, United States
Duration: Dec 15 2010Dec 17 2010

Other

Other2010 49th IEEE Conference on Decision and Control, CDC 2010
CountryUnited States
CityAtlanta, GA
Period12/15/1012/17/10

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All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization

Cite this

Chowdhary, G., & Johnson, E. (2010). Concurrent learning for convergence in adaptive control without persistency of excitation. In 2010 49th IEEE Conference on Decision and Control, CDC 2010 (pp. 3674-3679). [5717148] https://doi.org/10.1109/CDC.2010.5717148