Design of model predictive control for constrained Markov jump linear systems with multiplicative noises and online portfolio selection V. V. Dombrovskii, T. Pashinskaya
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ArticleContent type: Текст Media type: электронный Subject(s): рыночные трения | стохастические системы | марковские скачки | прогнозирующее управление | выбор портфеля | трансакционные издержкиGenre/Form: статьи в журналах Online resources: Click here to access online
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International journal of robust and nonlinear control Vol. 30, № 3. P. 1050-1070Abstract: In this paper, we consider model predictive control for a class of constrained
discrete-time Markov jump linear systems with multiplicative noises. A generalized
performance criterion is composed of a weighted sum of a linear
combination of the (a) expected value of quadratic forms of state and control
vectors, (b) quadratic forms of the expected value of the state vector, and (c) the
linear component of the expected value of the state vector. The goal of the present
paper is to design optimal control strategies subject to hard constraints on the
input manipulated variables and to provide a numerically tractable algorithm
for practical applications. The results are applied to a problem of online investment
portfolio selection. Our approach is tested on a set of a real data from the
New York Stock Exchange.
Библиогр.: 30 назв.
In this paper, we consider model predictive control for a class of constrained
discrete-time Markov jump linear systems with multiplicative noises. A generalized
performance criterion is composed of a weighted sum of a linear
combination of the (a) expected value of quadratic forms of state and control
vectors, (b) quadratic forms of the expected value of the state vector, and (c) the
linear component of the expected value of the state vector. The goal of the present
paper is to design optimal control strategies subject to hard constraints on the
input manipulated variables and to provide a numerically tractable algorithm
for practical applications. The results are applied to a problem of online investment
portfolio selection. Our approach is tested on a set of a real data from the
New York Stock Exchange.
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