Multiple-Horizon predictive control for Markov/switched linear systems

Henández-Mejías, Manuel A.; Sala, Antonio; Querol, Andrés; Ariño, Carlos

doi:10.1016/j.ifacol.2015.11.288

Cited by 5 publications

(2 citation statements)

References 14 publications

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“…Inspired by Henández‐Mejías et al [11], we first introduce the definition of coupled invariant sets as follows: Definition 1 For a discrete time MJLS

\begin{matrix} right left right left right left right left right left right left0.278em 2em 0.278em 2em 0.278em 2em 0.278em 2em 0.278em 2em 0.278em3pt \\ x_{k + 1} & = A (r_{k}) x_{k} + E (r_{k}) w_{k}, \end{matrix}

a family of sets

S ()(, i), i \in M

is said to be coupled invariant if

\forall x \in S ()(, i), i \in M

A ()(, i) x + E ()(, i) w \in S ()(, j), \forall w \in W, \forall j \in Γ ()(, i),

where

Γ ()(, i) ≜ (}{, j \in M : ρ_{i j} > 0), i \in M

.…”

Section: Problem Setup and Preliminary Resultsmentioning

confidence: 99%

“…To improve the control performance and enlarge the initial feasible region, MPC designs using the multi‐step feedback control law/interpolation control law have been proposed in [9, 10], respectively. While Henández‐Mejías et al [11] focused on the reduction of the computational load of the resulting MPC algorithms. Also, by applying dynamic programming, constrained MPC designs have also been addressed in [12].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Model predictive control synthesis for constrained Markovian jump linear systems with bounded disturbance

Lǚ

et al. 2017

IET Control Theory & Applications

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“…Inspired by Henández‐Mejías et al [11], we first introduce the definition of coupled invariant sets as follows: Definition 1 For a discrete time MJLS

\begin{matrix} right left right left right left right left right left right left0.278em 2em 0.278em 2em 0.278em 2em 0.278em 2em 0.278em 2em 0.278em3pt \\ x_{k + 1} & = A (r_{k}) x_{k} + E (r_{k}) w_{k}, \end{matrix}

a family of sets

S ()(, i), i \in M

is said to be coupled invariant if

\forall x \in S ()(, i), i \in M

A ()(, i) x + E ()(, i) w \in S ()(, j), \forall w \in W, \forall j \in Γ ()(, i),

where

Γ ()(, i) ≜ (}{, j \in M : ρ_{i j} > 0), i \in M

.…”

Section: Problem Setup and Preliminary Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Model predictive control synthesis for constrained Markovian jump linear systems with bounded disturbance

Lǚ

et al. 2017

IET Control Theory & Applications

View full text Add to dashboard Cite

Design of model predictive control for constrained Markov jump linear systems with multiplicative noises and online portfolio selection

Dombrovskii

Pashinskaya

2019

Intl J Robust & Nonlinear

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SummaryIn 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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Stable receding-horizon scenario predictive control for Markov-jump linear systems

Sala¹,

Hernández-Mejías²,

Ariño

2017

Automatica

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Multiple-Horizon predictive control for Markov/switched linear systems

Cited by 5 publications

References 14 publications

Model predictive control synthesis for constrained Markovian jump linear systems with bounded disturbance

Model predictive control synthesis for constrained Markovian jump linear systems with bounded disturbance

Design of model predictive control for constrained Markov jump linear systems with multiplicative noises and online portfolio selection

Stable receding-horizon scenario predictive control for Markov-jump linear systems

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