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本文的目的是研究随机时滞微分系统的稳定性和可镇定性。 为此,我们主要使用两种方法来列出随机时滞基准系统的稳定性和稳定性的必要条件和充分条件。 一种方式是在操作员频谱和表示方面。 另一种是通过李雅普诺夫方程的方法。 此外,我们介绍了随机时滞基准系统的不可移动频谱的概念,描述了时滞系统不可移动频谱的PBH准则,并研究了不可移动频谱与随机时滞基准系统的稳定性之间的关系。 。
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Research Article
The Stability and Stabilization of Stochastic Delay-Time Systems
Gang Li
1,2
and Ming Chen
1
1
School of Mathematics and System Science, Shandong University of Science and Technology, Qingdao 265590, China
2
College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 265590, China
Correspondence should be addressed to Gang Li; ligangccm@163.com
Received 17 December 2013; Revised 17 April 2014; Accepted 1 May 2014; Published 3 June 2014
Academic Editor: Wuquan Li
Copyright © 2014 G. Li and M. Chen. is is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
e aim of this paper is to investigate the stability and the stabilizability of stochastic time-delay deference system. To do this, we
use mainly two methods to give a list of the necessary and sucient conditions for the stability and stabilizability of the stochastic
time-delay deference system. One way is in term of the operator spectrum and -representation; the other is by Lyapunov equation
approach. In addition, we introduce the notion of unremovable spectrum of stochastic time-delay deference system, describe the
PBH criterion of the unremovable spectrum of time-delay system, and investigate the relation between the unremovable spectrum
and the stabilizability of stochastic time-delay deference system.
1. Introduction
e stochastic time-delay system is one of the fundamental
research branches in the theory of control systems, which is
usually applied in the elds of electronics, machinery, chemi-
cals, life sciences, economics, and so on. As is well known, the
stability is an essential concept in linear system theory, which
is relative to the system matrix root-clustering in subregions
of the complex plane, and also the spectral operator approach
iseectiveinthestudyoftheeigenvalueplacementofa
matrix (see [1]). Since two classic books [2, 3]appeared,
stochastic stability and stabilization of It
ˆ
o dierential systems
have been investigated by many researchers for several
decades; we refer the reader to [4–6] and the references
therein. More specically, for linear time-invariant stochastic
(LTIS) systems, most work is concentrated on the investi-
gation of mean square stabilization, which has important
applications in system analysis and design. Some necessary
and sucient conditions for the mean square stabilization
of LTIS systems have been obtained in terms of generalized
algebraic Riccati equation (GARE) or linear matrix inequality
(LMI) in [7–14] or spectra of some operators in [5, 9, 15].
For the stochastic delay-time systems, the present results
were mainly obtained by Lyapunov functional approach. We
concentrate our attention upon the stability and stabilization
of stochastic systems by the operator spectrum.
e structure of this paper is as follows. In Section 2,
with the aid of the operator spectrum, -representation, and
Lyapunov equation approach, some necessary and sucient
conditions are given for the stability and the stabilizability of
stochastic delay-time systems. In Section 3,theunremovable
spectrum of stochastic delay-time systems is introduced, and
PBH criterion of the stabilizability of stochastic delay-time
systems is presented.
For convenience, we adopt the following traditional
notations.
𝑛
: the set of all symmetric matrices, whose
components may be complex; ={0,1,2,...};
(Ker()):
the transpose (kernel space) of a matrix ; ≥0(>0)is a
positive semidenite (positive-denite) symmetric matrix ;
: identity matrix; (): spectral set of the operator or matrix
;
−
(
−0
): the open le (closed le) hand side complex
plane. (0,)={|<}; ⋅is the
2
-norm;
2
F
𝑡
(
+
,
𝑛
𝑥
): space of nonanticipative stochastic processes
() ∈
𝑛
𝑥
withrespecttoanincreasing-algebra {F
𝑡
}
𝑡≥0
satisfying ()
2
<∞. Finally, we make the assumption
throughout this paper that all systems have real coecients.
2. The Stability of Stochastic
Delay-Time Systems
In this section, we will investigate the stability and stabiliz-
ability of the stochastic time-delay deference system using
Hindawi Publishing Corporation
Mathematical Problems in Engineering
Volume 2014, Article ID 272745, 8 pages
http://dx.doi.org/10.1155/2014/272745
2 Mathematical Problems in Engineering
thespectrumofoperatorandLyapunovequationapproach.
At rst, we introduce a Lyapunove operator. Consider the
following linear dierence system with constant delays:
(
+1
)
=
0
(
)
+
0
(
)
(
)
+
𝑚
𝑗=1
𝑗
−+
𝑗
−
(
)
, ∈,
(1)
with the initial condition
(
)
=
(
)
, =0,−1,−2,...,−.
(2)
Here, ∈
𝑛
is a column vector,
𝑗
,
𝑗
∈
𝑛×𝑛
, =0,1,...,,
are constant coecient matrices, ()∈
𝑛
is a deterministic
initial condition, {()∈,∈}is a sequence of real
random variables dened on a complete probability space
{,F,F
𝑡
,}which is a wide sense stationary, second-order
process with (())=0and (()())=
𝑠,𝑡
,where
𝑠,𝑡
is
the Kronecker delta with F
𝑡
={():0≤≤}.
Denition 1. e trivial stationary solution () = 0of the
system (1) is called mean square stable if, for any arbitrarily
small number >0, there exists a number >0,when<
,suchthat
()
2
<,
(3)
for a solution ()=(,)of (1).
Denition 2. e trivial stationary solution () = 0of the
system (1)iscalledasymptoticallymeansquarestableifitis
stable in the sense of Denition 1 and, moreover, any solution
()=(,)of (1) satises
lim
𝑡→+∞
()
2
=0.
(4)
We consider the problem of nding criteria and sucient
conditions for the mean square asymptotic stability of the
trivial stationary solution ()=0by operator spectra. Since
the stochastic system (1) is a system with time-delays, it seems
impossible to construct the operator directly for (1)likethe
operator in [15].So,rstofall,weintroducethefollowing
column vector
()of new variables of dimension (+1):
(
)
=
(),
(−1),...,
(−)
.
(5)
e stochastic system (1) with time-delays can now be
written in the form of an equivalent stochastic system of
dimension (+1)without delay; namely,
(
+1
)
=
[
+
(
)
]
(
)
,
(6)
where and denote the following (+1)×(+1)
matrices:
=
0
1
⋅⋅⋅
𝑚−1
𝑚
0 ⋅⋅⋅ 0 0
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
0 0 ⋅⋅⋅ 0
,
=
0
1
⋅⋅⋅
𝑚−1
𝑚
00⋅⋅⋅00
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
00⋅⋅⋅00
.
(7)
If we set () =
()
(), ()satises the following
dierence equation:
(
+1
)
=
(
)
+
(
)
.
(8)
Motivated by (8), we introduce the following linear Lyapunov
operator:
L
𝐹,𝐺
:∈
𝑛(𝑚+1)
→
(
)
+
(
)
∈
𝑛(𝑚+1)
.
(9)
With the use of the Kronecker matrix product, the matrix
equation (8)canberewritteninthevectormatrixformas
follows:
(
+1
)
=
,
(10)
where
()denotes the
2
(+1)
2
-dimensional column
vector
(
)
=
1,1
(
)
,...,
1,𝑛
(
)
,...,
1,𝑛(𝑚+1)
(
)
,...,
𝑛(𝑚+1),𝑛(𝑚+1)
(
)
(11)
and
∈
𝑛
2
(𝑚+1)
2
×𝑛
2
(𝑚+1)
2
has the form
=⊗+⊗.
Now, we are in a position to give a spectral description for
the stability of system (1)by-representation in [14].
Lemma 3. Let
𝑛(𝑚+1)
be a
2
(+1)
2
×((+1)[(+1)+
1]/2)matrix and rank(
𝑛(𝑚+1)
)=((+1)[(+1)+1])/2;
then
𝑛(𝑚+1)
𝑛(𝑚+1)
is invertible.
eorem 4. e trivial solution ()=0of system (1) is
asymptotically mean square stable if and only if (L
𝐹,𝐺
)⊂
(0,1).
Proof. If we set ()=
()
(), ()satises
(
+1
)
=
(
)
+
(
)
,
(
)
=
(
)
(
)
∈
𝑛(𝑚+1)
,
=0,−1,−2,...,−, ∈.
(12)
Since (⋅)is real symmetric, (12) is a linear matrix equation
with (+1)[(+1)+1]/2dierent variables; that is, it is
in fact an (+1)[(+1)+1]/2th-order linear system. We
dene a map
L from
𝑛(𝑚+1)
to
𝑛(𝑚+1)[𝑛(𝑚+1)+1]/2
as follows.
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