The Method of Averaged Models for Discrete-Time Adaptive Systems
- Authors: Amelina N.O.1,2, Granichin O.N.1,2, Fradkov A.L.1,2
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Affiliations:
- St. Petersburg State University
- Institute for Problems in Mechanical Engineering
- Issue: Vol 80, No 10 (2019)
- Pages: 1755-1782
- Section: Topical Issue
- URL: https://journal-vniispk.ru/0005-1179/article/view/151183
- DOI: https://doi.org/10.1134/S0005117919100011
- ID: 151183
Cite item
Abstract
Dynamical processes in nature and technology are usually described by continuous-or discrete-time dynamical models, which have the form of nonlinear stochastic differential or difference equations. Hence, a topical problem is to develop effective methods for a simpler description of dynamical systems. The main requirement to simplification methods is preserving certain properties of a process under study. One group of such methods is represented by the methods of continuous- or discrete-time averagedmodels, which are surveyed in this paper. New results for stochastic networked systems are also introduced. As is shown below, the method of averaged models can be used to reduce the analytical complexity of a closed loop stochastic system. The corresponding upper bounds on the mean square distance between the states of an original stochastic system and its approximate averaged model are obtained.
About the authors
N. O. Amelina
St. Petersburg State University; Institute for Problems in Mechanical Engineering
Author for correspondence.
Email: natalia_amelina@mail.ru
Russian Federation, St. Petersburg; St. Petersburg
O. N. Granichin
St. Petersburg State University; Institute for Problems in Mechanical Engineering
Email: natalia_amelina@mail.ru
Russian Federation, St. Petersburg; St. Petersburg
A. L. Fradkov
St. Petersburg State University; Institute for Problems in Mechanical Engineering
Email: natalia_amelina@mail.ru
Russian Federation, St. Petersburg; St. Petersburg
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