Journal of Automation and Information Sciences
Publicado 12 números por año
ISSN Imprimir: 1064-2315
ISSN En Línea: 2163-9337
SJR:
0.173
SNIP:
0.588
CiteScore™::
2
Indexed in
Convergence of a Matrix Gradient Control Algorithm with Feedback Under Constraints
Volumen 32,
Edición 10, 2000,
pp. 35-45
DOI: 10.1615/JAutomatInfScien.v32.i10.50
SINOPSIS
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.
PALABRAS CLAVE: extremal problem, control algorithm, feedback, matrix minimization, matrix extremal, Lyapunov function.
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Index, Volume 52, 2020