2014
DOI: 10.1016/j.automatica.2014.09.007
|View full text |Cite
|
Sign up to set email alerts
|

On the quadratic stability of switched linear systems associated with symmetric transfer function matrices

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
4
0

Year Published

2016
2016
2024
2024

Publication Types

Select...
6
1

Relationship

0
7

Authors

Journals

citations
Cited by 11 publications
(4 citation statements)
references
References 12 publications
0
4
0
Order By: Relevance
“…De…nition 3.1. [12] An m m rational transfer function matrix G(s) is said to be strictly positive real (SPR) if there exists a real scalar > 0 such that G(s) is analytic for Re(s)…”
Section: Formulationmentioning
confidence: 99%
See 2 more Smart Citations
“…De…nition 3.1. [12] An m m rational transfer function matrix G(s) is said to be strictly positive real (SPR) if there exists a real scalar > 0 such that G(s) is analytic for Re(s)…”
Section: Formulationmentioning
confidence: 99%
“…with A r(t) = A + 1 N (1 r(t))BD 1 C and random signal r(t) 2 M = f1; 2; :::; N g. This system is represented schematically as We start a stochastic counterpart of the results obtained in [11] and [12] by considering a N states Markovian switched controller of the system in order to be able to perform some useful simulations, and prove stochastic stability under a Markovian transition rule rather than an arbitrary switching. Which means that we deal with the weaker notion of stability than the guaranteed one considered in [11].…”
Section: Chafai Im Zegouanmentioning
confidence: 99%
See 1 more Smart Citation
“…So, a classical approach for stability analysis of arbitrary switched linear systems is to find a CLF for all subsystems. In many research work such as [11–14] and the references therein, stability analysis of switched linear systems under arbitrary switching has been investigated using CLF in the quadratic form. Expressing the stability conditions in terms of linear matrix inequalities (LMIs) is the main advantage of applying common quadratic Lyapunov function (CQLF).…”
Section: Introductionmentioning
confidence: 99%