Iterative learning approach to active noise control of highly autocorrelated signals with applications to machinery noise

Adam Lasota; Michał Meller

doi:10.1049/iet-spr.2020.0064

Iterative learning approach to active noise control of highly autocorrelated signals with applications to machinery noise

Abstrakt

This paper discusses the design and application of iterative learning control (ILC) and repetitive control (RC) for high modal density systems. Typical examples of these systems are structural and acoustical systems considered in active structural acoustic control (ASAC) and active noise control (ANC) applications. The application of traditional ILC and RC design techniques, which are based on a parametric system model, on systems with a high modal density has several important drawbacks: the design procedure is complex, the controllers require much computational power and the robustness of the controllers is low. This paper describes a novel strategy to design noncausal ILC and RC filters, which is especially suited for high modal density systems. Since it does not require a parametric system model, the novel strategy avoids several drawbacks of the traditional techniques: no cumbersome parametric model estimation is required; the ILC and RC controllers are robust to small changes of the poles and zeros of the controlled system; and the complexity of the ILC and RC control filters is restricted. A crucial element in the proposed strategy is the noncausal filtering in the ILC and RC controllers, which requires the availability of a trigger signal to announce a new ILC trial or RC period in advance. A numerical validation on a simulation model proves the potential of the developed strategy.

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Słowa kluczowe

ITERATIVE LEARNING CONTROL REPETITIVE CONTROL HIGH NUMBER OF DEGREES OF FREEDOM

Informacje szczegółowe

Kategoria:: Publikacja w czasopiśmie
Typ:: artykuły w czasopismach
Opublikowano w:: IET Signal Processing nr 14, strony 560 - 568,
ISSN: 1751-9675
Język:: angielski
Rok wydania:: 2020
Opis bibliograficzny:: Lasota A., Meller M.: Iterative learning approach to active noise control of highly autocorrelated signals with applications to machinery noise// IET Signal Processing -Vol. 14,iss. 8 (2020), s.560-568
DOI:: Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1049/iet-spr.2020.0064
Weryfikacja:: Politechnika Gdańska