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Journal of Automation and Information Sciences
SJR: 0.232 SNIP: 0.464 CiteScore™: 0.27

ISSN Imprimer: 1064-2315
ISSN En ligne: 2163-9337

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Journal of Automation and Information Sciences

DOI: 10.1615/JAutomatInfScien.v32.i10.50
pages 35-45

Convergence of a Matrix Gradient Control Algorithm with Feedback Under Constraints

Yarema I. Zyelyk
Institute of Space Research of National Academy of Sciences of Ukraine and National Space Agency of Ukraine, Kyiv, Ukraine

RÉSUMÉ

A matrix stochastic regularizing controlling algorithm with feedback for solution of an ill-posed extremum problem is proposed. This algorithm is applicable to a linear control object of arbitrary finite dimension in the presence of additive multidimensional uncorrelated noise under non-stochastic constraints on the unknown matrix of the object's parameters and matrix inputs. The classes of controlled objects and non-controlled disturbances for which the algorithm converges in asymptotic to the fixed point of the algorithm-generating mapping with probability unity, are established.