Projects per year
Abstract
Recently, Mao [1] proposed a kind of feedback control based on discrete time
state observations to stabilize continuous-time hybrid stochastic systems
in mean-square sense. We find that the feedback control there still depends
on the continuous-time observations of the mode. However, it usually costs to
identify the current mode of the system in practice. So we can further improve
the control to reduce the control cost by identifying the mode at discrete times
when we make observations for the state. In this paper, we aim to design such
a type of feedback controls based on the discrete-time observations of both
state and mode to stabilize the given unstable hybrid stochastic differential
equations (SDEs) in the sense of mean-square exponential stability. Moreover,
a numerical example is given to illustrate our results.
state observations to stabilize continuous-time hybrid stochastic systems
in mean-square sense. We find that the feedback control there still depends
on the continuous-time observations of the mode. However, it usually costs to
identify the current mode of the system in practice. So we can further improve
the control to reduce the control cost by identifying the mode at discrete times
when we make observations for the state. In this paper, we aim to design such
a type of feedback controls based on the discrete-time observations of both
state and mode to stabilize the given unstable hybrid stochastic differential
equations (SDEs) in the sense of mean-square exponential stability. Moreover,
a numerical example is given to illustrate our results.
Original language | English |
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Pages (from-to) | 1-10 |
Number of pages | 10 |
Journal | Asian Journal of Control |
Early online date | 26 Apr 2017 |
DOIs | |
Publication status | E-pub ahead of print - 26 Apr 2017 |
Keywords
- Brownian motion
- Markov chain
- mean-square exponential stability
- discrete-time feedback control
Projects
- 1 Finished
-
Numerical Analysis of Stochastic Differential Equations: New Challenges
1/10/15 → 30/09/17
Project: Research Fellowship