Exponential Stabilisation of Continuous-time Periodic Stochastic Systems by Feedback Control Based on Periodic Discrete-time Observations

Ran Dong, Xuerong Mao, Stewart Birrell

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Abstract

Since Mao in 2013 discretised the system observations for stabilisation problem of hybrid SDEs (stochastic differential equations with Markovian switching) by feedback control, the study of this topic using a constant observation frequency has been further developed. However, time-varying observation frequencies have not been considered. Particularly, an observational more efficient way is to consider the time-varying property of the system and observe a periodic SDE system at the periodic time-varying frequencies. This study investigates how to stabilise a periodic hybrid SDE by a periodic feedback control, based on periodic discrete-time observations. This study provides sufficient conditions under which the controlled system can achieve pth moment exponential stability for p > 1 and almost sure exponential stability. Lyapunov's method and inequalities are main tools for derivation and analysis. The existence of observation interval sequences is verified and one way of its calculation is provided. Finally, an example is given for illustration. Their new techniques not only reduce observational cost by reducing observation frequency dramatically but also offer flexibility on system observation settings. This study allows readers to set observation frequencies according to their needs to some extent.

Original languageEnglish
Pages (from-to)909-919
Number of pages11
JournalIET Control Theory and Applications
Volume14
Issue number6
Early online date6 Jan 2020
DOIs
Publication statusPublished - 16 Apr 2020

Keywords

  • Stochastic differential equations
  • exponential stabilisation
  • Markovian switching
  • Periodic stochastic systems
  • Feedback control
  • discrete-time observations

ASJC Scopus subject areas

  • Control and Optimization
  • Human-Computer Interaction
  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Computer Science Applications

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