For linear multivariable systems, we construct discrete output controllers that guarantee a given stability margin radius on the input or output of a control plant. Besides, given control time is also taken into account. We show that solving such problems reduces to a certain specially constructed standard H∞-optimization problem. A numerical solution has been implemented in MATLAB with the Robust Control Toolbox suite based on the method of linear matrix inequalities (LMI).

Consideration was given to the problem of relative positioning with the use of variable magnetic field. Preliminary calibration of the positioning system was shown to be necessary to increase precision in determination of the navigation parameters. Various approaches to the calibration experiment were discussed. Efficient calibration algorithms were proposed, and a numerical example of solving a calibration problem was presented

Рассматриваются вопросы идентификации оборудования в информационно-измерительных системах. Исследуются вопросы устойчивости и точности работы алгоритмов оценивания в замкнутом контуре. Рассматривается влияние действия оператора на процессы идентификации и управления.

Аппарат линейных матричных неравенств применяется к решению классической задачи о линейно-квадратичном регуляторе. Показано, каким образом эта техника может приводить к оптимальному решению, получаемому через уравнение Риккати. Рассмотрены некоторые нежелательные эффекты, возникающие при использовании иных методов решения, известных из литературы; приведены численные примеры.

A column-based classifier as a method for pattern recognition and clustering built on the notion of inverse patterns is considered. This approach replaces the matching of unknown patterns to prototype patterns with the intersection operations for inverse images. These operations are much less computationally intensive than comparison operations. A possible analogy with hashing is that the features of the pattern being recognized are used as addresses that play the role of hash function arguments that define the name of the pattern class without search operations like in hashing. As an example of a practical implementation of the proposed approach the recognition problem for dynamically changing patterns represented by video clips is solved