Output feedback concurrent learning model reference adaptive control

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

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

Abstract

Concurrent learning model reference adaptive control has recently been shown to guarantee simultaneous state tracking and parameter estimation error convergence to zero without requiring the restrictive persistency of excitation condition of other adaptive methods. This simultaneous convergence drastically improves the transient performance of the adaptive system since the true model is learned, but prior results were limited to systems with full state feedback. This paper presents an output feedback form of the concurrent learning controller for a novel extension to partial state feedback systems. The approach modifies a baseline LQG/LTR adaptive law with a recorded data stack of output and state estimate vectors. This maintains the guaranteed stability and boundedness of the baseline adaptive method, while improving output tracking error response. Simulations of exible aircraft dynamics demonstrate the improvement of the concurrent learning system over a baseline output feedback adaptive method.

Original languageEnglish (US)
Title of host publicationAIAA Guidance, Navigation, and Control Conference
PublisherAmerican Institute of Aeronautics and Astronautics Inc, AIAA
ISBN (Print)9781624103391
StatePublished - Jan 1 2015
Externally publishedYes
EventAIAA Guidance, Navigation, and Control Conference, 2015 - Kissimmee, United States
Duration: Jan 5 2015Jan 9 2015

Publication series

NameAIAA Guidance, Navigation, and Control Conference, 2013

Other

OtherAIAA Guidance, Navigation, and Control Conference, 2015
CountryUnited States
CityKissimmee
Period1/5/151/9/15

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Aerospace Engineering
  • Control and Systems Engineering

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