ROBUST STATIC OUTPUT FEEDBACK STACKELBERG STRATEGY FOR MARKOV JUMP DELAY STOCHASTIC SYSTEMS

In this study, a robust static output feedback (SOF) Stackelberg strategy for a class of uncertain Markov Jump linear stochastic delay systems (UMJLSDSs) is investigated. After introducing certain preliminaries, a SOF Stackelberg strategy is derived. It is shown that the strategy set is established...

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Published inAnnals. Series on mathematics and its applications Vol. 12; no. 1-2; pp. 476 - 500
Main Authors Mukaidani, Hiroaki, Saravanakumar, Ramasamy, Xu, Hua , Zhuang, Weihua
Format Journal Article
LanguageEnglish
Published 2020
Online AccessGet full text
ISSN2066-5997
2066-6594
DOI10.56082/annalsarscimath.2020.1-2.476

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Abstract In this study, a robust static output feedback (SOF) Stackelberg strategy for a class of uncertain Markov Jump linear stochastic delay systems (UMJLSDSs) is investigated. After introducing certain preliminaries, a SOF Stackelberg strategy is derived. It is shown that the strategy set is established by solving two constraint optimization problems and cross-coupled stochastic matrix equations that consist of bilinear matrix inequalities (BMIs). In order to obtain the corresponding solutions of the constraint optimization problems and cross coupled stochastic matrix equations (CCSMEs), an algorithm based on the Krasnoselskii iterative algorithm is proposed instead of solving BMI. It is also shown that weak convergence can be achieved using this approach. A practical example is provided to demonstrate the effectiveness and convergence of the proposed algorithm.
AbstractList In this study, a robust static output feedback (SOF) Stackelberg strategy for a class of uncertain Markov Jump linear stochastic delay systems (UMJLSDSs) is investigated. After introducing certain preliminaries, a SOF Stackelberg strategy is derived. It is shown that the strategy set is established by solving two constraint optimization problems and cross-coupled stochastic matrix equations that consist of bilinear matrix inequalities (BMIs). In order to obtain the corresponding solutions of the constraint optimization problems and cross coupled stochastic matrix equations (CCSMEs), an algorithm based on the Krasnoselskii iterative algorithm is proposed instead of solving BMI. It is also shown that weak convergence can be achieved using this approach. A practical example is provided to demonstrate the effectiveness and convergence of the proposed algorithm.
Author Mukaidani, Hiroaki
Xu, Hua
Zhuang, Weihua
Saravanakumar, Ramasamy
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CorporateAuthor Graduate School of Business Sciences, The University of Tsukuba
Graduate School of Advanced Science and Engineering, Hiroshima University
Department of Electrical and Computer Engineering, University of Waterloo, 200 University Avenue West, Waterloo
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