Pareto optimal control for uncertain Markov jump linear stochastic systems

Hiroaki Mukaidani, Ippo Ishibashi, Shouhei Furuya

Research output: Contribution to journalConference article

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 languageEnglish
JournalInternational Conference of Control, Dynamic Systems, and Robotics
DOIs
Publication statusPublished - 2017
Externally publishedYes
Event4th International Conference of Control, Dynamic Systems, and Robotics, CDSR 2017 - Toronto, Canada
Duration: Aug 21 2017Aug 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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