2017
DOI: 10.1049/iet-cta.2017.0316
|View full text |Cite
|
Sign up to set email alerts
|

Dynamic output‐feedback stabilisation for Markovian jump systems with incomplete transition description and input quantisation: linear matrix inequality approach

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1

Citation Types

0
3
0

Year Published

2019
2019
2020
2020

Publication Types

Select...
3

Relationship

1
2

Authors

Journals

citations
Cited by 3 publications
(3 citation statements)
references
References 36 publications
0
3
0
Order By: Relevance
“…first provided a new method, which was combined with subsystems' Lyapunov functions and topological structure of networks to construct a global Lyapunov function. Compared with some references [31, 32], which used the linear matrix inequalities method, the method we use can not only help us constructing a suitable Lyapunov function which is related to the topological structure of networks, but also avoid solving linear matrix inequalities. Remark 4 In recent years, many scholars have investigated the dynamical behaviours of delayed systems such as stabilisation [18, 33] and synchronisation [25, 34]. In [33, 34], they restricted the range of time‐varying delays false(τ˙false(tfalse)<τfalse~<1false).…”
Section: Resultsmentioning
confidence: 99%
“…first provided a new method, which was combined with subsystems' Lyapunov functions and topological structure of networks to construct a global Lyapunov function. Compared with some references [31, 32], which used the linear matrix inequalities method, the method we use can not only help us constructing a suitable Lyapunov function which is related to the topological structure of networks, but also avoid solving linear matrix inequalities. Remark 4 In recent years, many scholars have investigated the dynamical behaviours of delayed systems such as stabilisation [18, 33] and synchronisation [25, 34]. In [33, 34], they restricted the range of time‐varying delays false(τ˙false(tfalse)<τfalse~<1false).…”
Section: Resultsmentioning
confidence: 99%
“…+ẋ T (t)Σ 22ẋ (t) + 2σ T (t)(∇u(t) + u c (t)). (19) Using the conditions (3) and (16), it is shown that u c (t) ensures that the last term in (19) is negative, i.e.,…”
Section: A Admissibility Analysis For Closed-loop Systemmentioning
confidence: 99%
“…Sometimes these can also cause stable closed-loop system unstable [11]. For this reason, the stabilization problems of the systems with quantized input have been widely studied for various systems [12]- [16]. In the existing studies, there are two main types of static quantizers: logarithmic and uniform.…”
Section: Introductionmentioning
confidence: 99%