name
Raskina Anastasia Vladimirovna
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Research interests
системный анализ, непараметрическая идентификация, адаптивное управление
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Articles count: 2
Сформировать список работ, опубликованных в Научном журнале КубГАУ
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NONPARAMETRIC ADAPTIVE DUAL CIRCUIT CONTROL OF DYNAMIC PROCESSES UNDER PARTIAL UNCERTAINTY
Description
The article considers the tasks of nonparametric dual control of dynamic objects with discrete-continuous nature of the process is considered. In this case, the only value of memory depth of dynamic processes is known, but the parametric structure of the model is partially unknown. The nonparametric algorithms of adaptive dual control for external control loop were offered. The proposed loop of control is designed for systems, which include in technological scheme internal control loop, specifically a standard controller. In solving this problem, the methods of nonparametric identification theory, control theory, the theory of adaptive systems, mathematical statistics and statistical modeling are used. The theoretical information of the non-parametric algorithms of dual adaptive control under conditions of incomplete information of the process is produced. The essential difference between the dual control algorithms from the standard is that the nonparametric control unit performs two functions: research and control of the process of active accumulation of information. The computational experiments show that the introduction of the proposed scheme significantly improves the quality of control, and the existing control system in operating controls are maintained
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TO THE NONPARAMETRIC IDENTIFICATION OF TECHNOLOGICAL PROCESS OF SEQUENCE OBJECTS
Description
The task of nonparametric identification of sequence objects with discrete-continuous nature of the process under nonparametric uncertainty, i.e. in conditions where a priori information is not sufficient for an informed choice of a model structure up to parameters is considered. Among series-connected objects, there can be objects both dynamic and instantaneous ones with a lag. This kind of technological chains is common in various industries, particularly in metal, power, oil refining, etc. in solving this problem were used methods of nonparametric identification theory, mathematical statistics and statistical modeling. The theory of nonparametric systems is based on local approximation methods, in particular algorithms for nonparametric estimation of different kind of dependency from observation of input-output variables of the object. The article presents a nonparametric model for the group of spinning objects with delay. In the work we show in detail the results of numerical studies showing that the use of nonparametric algorithms allows predicting process performance with sufficient accuracy
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