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A clarifying analysis of feedback error learning in an LTI framework
Author(s) -
Nilsson Magnus,
Egardt Bo
Publication year - 2008
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.1031
Subject(s) - feed forward , control theory (sociology) , normalization (sociology) , estimator , adaptive control , lti system theory , computer science , invariant (physics) , regular polygon , convex combination , stability (learning theory) , equivalence (formal languages) , convergence (economics) , feedback control , mathematics , control (management) , convex optimization , linear system , artificial intelligence , engineering , control engineering , machine learning , statistics , discrete mathematics , anthropology , mathematical analysis , sociology , mathematical physics , geometry , economic growth , economics
Feedback error learning (FEL) is a proposed technique for reference‐feedforward adaptive control. FEL in a linear and time‐invariant (LTI) framework has been studied recently; the studies can be seen as proposed solutions to a ‘feedforward MRAC’ problem. This paper reanalyzes two suggested schemes with new interpretations and conclusions. It motivates the suggestion of an alternative scheme for reference‐feedforward adaptive control, based on a certainty‐equivalence approach. The suggested scheme differs from the analyzed ones by a slight change in both the estimator and the control law. Boundedness and error convergence are then guaranteed when the estimator uses normalization combined with parameter projection onto a convex set where stability of the estimated closed‐loop system holds. Copyright © 2008 John Wiley & Sons, Ltd.