Optimal predictive control strategies for systems with random parameters described by multidimensional Markov switching regression model | Vestnik Tomskogo gosudarstvennogo universiteta. Upravlenie, vychislitelnaja tehnika i informatika – Tomsk State University Journal of Control and Computer Science. 2019. № 48. DOI: 10.17223/19988605/48/1

Optimal predictive control strategies for systems with random parameters described by multidimensional Markov switching regression model

We consider a class of discrete stochastic systems with parameters whose evolution is described by the multidimensional regression equation with Markov jumps. The dynamics of exogenous factors is described by a vector autoregressive model with Markov switching of regimes of order p (MS-VAR (p) model). The optimal strategies for predictive control have been synthesized taking into account explicit restrictions on control variables according to a generalized criterion, which is a linear combination; a) the expected values of quadratic forms in state and control; b) the quadratic form of the expected values of the states of the system; c) linear part - the expected value of the state of the system.

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Keywords

стохастические системы, марковские скачки, многомерная модель регрессии, прогнозирующее управление, ограничения, stochastic systems, Markov jumps, multidimensional regression, model predictive control, constrains

Authors

Name	Organization	E-mail
Dombrovskii Vladimir V.	Tomsk State University	dombrovs@ef.tsu.ru
Pashinskaya Tatiana Yu.	Tomsk State University	tatyana.obedko@mail.ru

Всего: 2

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