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Effective identification algorithms for adaptive control
Author(s) -
Bittanti Sergio,
Campi Marco,
Lorito Fabrizio
Publication year - 1992
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.4480060310
Subject(s) - subspace topology , identification (biology) , algorithm , adaptive control , noise (video) , recursive least squares filter , computer science , system identification , projection (relational algebra) , tracking (education) , least squares function approximation , control theory (sociology) , dykstra's projection algorithm , mathematics , adaptive filter , control (management) , artificial intelligence , statistics , data mining , psychology , pedagogy , botany , estimator , image (mathematics) , biology , measure (data warehouse)
Minimum variance adaptive control schemes are considered. By means of the concept of excitation subspace, the notion of effective identification algorithm is introduced. It is shown that, if the system to be controlled is noise‐free and minimum phase, the tracking error tends to zero provided that the identification is effective. Finally, the effectiveness of the most popular recursive identification techniques (recursive least squares, stochastic gradient, projection algorithm) is discussed.