Methods of modeling and probabilistic analysis of large deviations of dynamic systems

Autor:	Aleksey Kabanov, S. A. Dubovik
Rok vydání:	2020
Předmět:	History Computer science Large deviations theory Probabilistic analysis of algorithms Algorithm Computer Science Applications Education
Zdroj:	Journal of Physics: Conference Series. 1661:012044
ISSN:	1742-6596 1742-6588
DOI:	10.1088/1742-6596/1661/1/012044
Popis:	The paper presents a new method of analyzing large deviations for nonlinear systems defined through matrices with state-dependent coefficients. Large deviations of the controlled process from some standard state is the basis to forecast any critical situation. The task of forecasting is limited to the optimal control problem of Lagrange-Pontryagin optimal control. Two effective methods – State-Dependent Riccati Equations (SDRE) and approximated sequence of Riccati equations (ASRE) – are used. The presented approach to the Lagrange-Pontryagin problem differs from the approach previously used for linear cases by the fact that it uses control in the form of a feedback instead of software open-loop control. This eliminates the need to calculate the end-time boundary value for a conjugate variable, which is the most time-consuming task in nonlinear cases.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::552ae1c5c925259b9a591c5cfdd50b8f https://doi.org/10.1088/1742-6596/1661/1/012044 Zobrazit plný text záznamu