Static output feedback strategy for mean-field social control with nonlinear stochastic dynamics
A mean-field social control problem for uncertain nonlinear stochastic systems is investigated by using a robust static output feedback (SOF) strategy. First, the problem in the single decision maker case is investigated in terms of guaranteed cost control approaches to derive suboptimal conditions...
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| Vydáno v: | International journal of systems science Ročník 56; číslo 11; s. 2795 - 2816 |
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| Jazyk: | angličtina |
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Taylor & Francis
18.08.2025
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| ISSN: | 0020-7721, 1464-5319 |
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| Abstract | A mean-field social control problem for uncertain nonlinear stochastic systems is investigated by using a robust static output feedback (SOF) strategy. First, the problem in the single decision maker case is investigated in terms of guaranteed cost control approaches to derive suboptimal conditions at the supremum of the cost function. The Karush-Kuhn-Tucker (KKT) condition is used to derive the necessary conditions which are expressed as a large stochastic combined matrix equation (SCME). Second, the preliminary results in the single decision maker case are used to study the Pareto optimal strategy in a cooperative game. As our main contribution, we derive the high-order centralised strategies and the low-order decentralised strategies, respectively, for the cooperative game. In order to avoid the difficulty of higher-order dimensional problem related to SCMEs, a new reduced-order decomposition numerical scheme by means of Newton's method is developed. The computation for designing the proposed strategy set can be performed in low dimension, even when the number of decision makers approachs to infinity. Moreover, the degradation of the cost function is rigorously evaluated by comparing the centralised strategy set with the proposed strategy set. Finally, several numerical experiments are conducted to demonstrate the usefulness and effectiveness of the proposed strategy set. |
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| AbstractList | A mean-field social control problem for uncertain nonlinear stochastic systems is investigated by using a robust static output feedback (SOF) strategy. First, the problem in the single decision maker case is investigated in terms of guaranteed cost control approaches to derive suboptimal conditions at the supremum of the cost function. The Karush-Kuhn-Tucker (KKT) condition is used to derive the necessary conditions which are expressed as a large stochastic combined matrix equation (SCME). Second, the preliminary results in the single decision maker case are used to study the Pareto optimal strategy in a cooperative game. As our main contribution, we derive the high-order centralised strategies and the low-order decentralised strategies, respectively, for the cooperative game. In order to avoid the difficulty of higher-order dimensional problem related to SCMEs, a new reduced-order decomposition numerical scheme by means of Newton's method is developed. The computation for designing the proposed strategy set can be performed in low dimension, even when the number of decision makers approachs to infinity. Moreover, the degradation of the cost function is rigorously evaluated by comparing the centralised strategy set with the proposed strategy set. Finally, several numerical experiments are conducted to demonstrate the usefulness and effectiveness of the proposed strategy set. |
| Author | Xu, Hua Zhuang, Weihua Mukaidani, Hiroaki |
| Author_xml | – sequence: 1 givenname: Hiroaki surname: Mukaidani fullname: Mukaidani, Hiroaki email: mukaida@hiroshima-u.ac.jp organization: Hiroshima University – sequence: 2 givenname: Hua surname: Xu fullname: Xu, Hua organization: The University of Tsukuba – sequence: 3 givenname: Weihua surname: Zhuang fullname: Zhuang, Weihua organization: University of Waterloo |
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| Cites_doi | 10.1109/TCST.87 10.1109/TAC.2020.2996189 10.1109/TPWRS.2004.825829 10.1109/LCSYS.2021.3135754 10.1007/s11424-021-1266-y 10.1109/TAC.2016.2579264 10.1002/rnc.v27.16 10.1016/j.automatica.2020.109272 10.1007/s00285-016-1086-1 10.1016/j.automatica.2020.109067 10.1109/TAC.9 10.1016/j.jmaa.2009.06.018 10.1016/0005-1098(95)00048-2 10.1016/0005-1098(95)00178-6 10.1016/j.arcontrol.2016.09.014 10.1007/s00245-021-09817-0 10.1002/asjc.v26.2 10.1016/j.ifacol.2022.09.335 10.1016/j.automatica.2018.03.017 10.1016/j.automatica.2018.08.008 10.1016/j.automatica.2014.12.015 10.1002/oca.v30:4 10.1016/j.automatica.2020.109088 10.1016/j.automatica.2020.108835 10.1109/TAC.2020.3036246 10.1016/S0167-6911(97)00014-5 10.1007/978-1-4471-6606-1 10.2514/1.16794 10.1109/TAC.2007.904450 10.1137/21M1414140 10.1016/j.automatica.2007.04.025 10.1016/j.automatica.2019.108774 10.1007/BF01389624 10.1016/j.automatica.2020.108951 10.1109/TFUZZ.2023.3330297 |
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| SubjectTerms | computational algorithm Mean-field social control multiple decision makers reduced-order technique uncertain nonlinear stochastic system |
| Title | Static output feedback strategy for mean-field social control with nonlinear stochastic dynamics |
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