Pareto optimal control for uncertain Markov jump linear stochastic systems

Hiroaki Mukaidani; Ippo Ishibashi; Shouhei Furuya

doi:10.11159/cdsr17.102

Pareto optimal control for uncertain Markov jump linear stochastic systems

Hiroaki Mukaidani, Ippo Ishibashi, Shouhei Furuya

Research output: Contribution to journal › Conference article › peer-review

Abstract

In this paper, a Pareto optimal strategy for uncertain Markovian linear stochastic system with multiple decision makers is investigated. By applying the guaranteed cost control principle, a set of conditions, wherein the stochastic system is exponentially mean-square stable (EMSS) and has a cost bound, is obtained using the stochastic algebraic Riccati inequality (SARI). In addition, the minimization problem of the cost bound is formulated. It is shown that the necessary conditions can be derived by a set of cross-coupled stochastic Riccati equations (CCSAREs).

Original language	English
Journal	International Conference of Control, Dynamic Systems, and Robotics
DOIs	https://doi.org/10.11159/cdsr17.102
Publication status	Published - 2017
Externally published	Yes
Event	4th International Conference of Control, Dynamic Systems, and Robotics, CDSR 2017 - Toronto, Canada Duration: Aug 21 2017 → Aug 23 2017

Keywords

Karush-Kuhn-Tucker (KKT) conditions
Pareto optimal control
Stochastic algebraic Riccati inequality (SARI)
Uncertain Markovian jump linear stochastic systems

ASJC Scopus subject areas

Artificial Intelligence
Control and Optimization
Control and Systems Engineering

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10.11159/cdsr17.102

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title = "Pareto optimal control for uncertain Markov jump linear stochastic systems",

abstract = "In this paper, a Pareto optimal strategy for uncertain Markovian linear stochastic system with multiple decision makers is investigated. By applying the guaranteed cost control principle, a set of conditions, wherein the stochastic system is exponentially mean-square stable (EMSS) and has a cost bound, is obtained using the stochastic algebraic Riccati inequality (SARI). In addition, the minimization problem of the cost bound is formulated. It is shown that the necessary conditions can be derived by a set of cross-coupled stochastic Riccati equations (CCSAREs).",

keywords = "Karush-Kuhn-Tucker (KKT) conditions, Pareto optimal control, Stochastic algebraic Riccati inequality (SARI), Uncertain Markovian jump linear stochastic systems",

author = "Hiroaki Mukaidani and Ippo Ishibashi and Shouhei Furuya",

note = "Publisher Copyright: {\textcopyright} 2016 Avestia Publishing.; 4th International Conference of Control, Dynamic Systems, and Robotics, CDSR 2017 ; Conference date: 21-08-2017 Through 23-08-2017",

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language = "English",

journal = "International Conference of Control, Dynamic Systems, and Robotics",

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TY - JOUR

T1 - Pareto optimal control for uncertain Markov jump linear stochastic systems

AU - Mukaidani, Hiroaki

AU - Ishibashi, Ippo

AU - Furuya, Shouhei

PY - 2017

Y1 - 2017

N2 - In this paper, a Pareto optimal strategy for uncertain Markovian linear stochastic system with multiple decision makers is investigated. By applying the guaranteed cost control principle, a set of conditions, wherein the stochastic system is exponentially mean-square stable (EMSS) and has a cost bound, is obtained using the stochastic algebraic Riccati inequality (SARI). In addition, the minimization problem of the cost bound is formulated. It is shown that the necessary conditions can be derived by a set of cross-coupled stochastic Riccati equations (CCSAREs).

AB - In this paper, a Pareto optimal strategy for uncertain Markovian linear stochastic system with multiple decision makers is investigated. By applying the guaranteed cost control principle, a set of conditions, wherein the stochastic system is exponentially mean-square stable (EMSS) and has a cost bound, is obtained using the stochastic algebraic Riccati inequality (SARI). In addition, the minimization problem of the cost bound is formulated. It is shown that the necessary conditions can be derived by a set of cross-coupled stochastic Riccati equations (CCSAREs).

KW - Karush-Kuhn-Tucker (KKT) conditions

KW - Pareto optimal control

KW - Stochastic algebraic Riccati inequality (SARI)

KW - Uncertain Markovian jump linear stochastic systems

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DO - 10.11159/cdsr17.102

M3 - Conference article

AN - SCOPUS:85053345381

SN - 2368-5433

JO - International Conference of Control, Dynamic Systems, and Robotics

JF - International Conference of Control, Dynamic Systems, and Robotics

T2 - 4th International Conference of Control, Dynamic Systems, and Robotics, CDSR 2017

Y2 - 21 August 2017 through 23 August 2017

ER -

Pareto optimal control for uncertain Markov jump linear stochastic systems

Abstract

Keywords

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