Name
Raskina Anastasia Vladimirovna
Scholastic degree
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Academic rank
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Honorary rank
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Organization, job position
Siberian Federal University
Web site url
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Articles count: 2
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
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