Mathematical Modeling, Analysis, and Advanced Control of Complex Dynamical Systems
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Jumei Wei, Rui Ma, "Stability and Stabilization of ContinuousTime Markovian Jump Singular Systems with Partly Known Transition Probabilities", Mathematical Problems in Engineering, vol. 2014, Article ID 752626, 8 pages, 2014. https://doi.org/10.1155/2014/752626
Stability and Stabilization of ContinuousTime Markovian Jump Singular Systems with Partly Known Transition Probabilities
Abstract
This paper investigates the problem of the stability and stabilization of continuoustime Markovian jump singular systems with partial information on transition probabilities. A new stability criterion which is necessary and sufficient is obtained for these systems. Furthermore, sufficient conditions for the state feedback controller design are derived in terms of linear matrix inequalities. Finally, numerical examples are given to illustrate the effectiveness of the proposed methods.
1. Introduction
In practice, many dynamical systems cannot be represented by the class of linear timeinvariant model since the dynamics of these systems are random with some features, for example, abrupt changes, breakdowns of components, changes in the interconnections of subsystems, and so forth. Such class of dynamical systems can be adequately described by the class of stochastic hybrid systems. A special class of hybrid systems referred to as Markovian jump systems (MJS), a class of multimodel systems in which the transitions among different modes are governed by a Markov chain, have attracted a lot of researchers and many problems have been solved, such as stability, stabilization, and control problems; see [1–7].
However, in most of the studies, complete knowledge of the mode transitions is required as a prerequisite for analysis and synthesis of MJS. This means that the transition probabilities of the underlying Markov chain are assumed to be completely known. However, in practice, incomplete transition probabilities are often encountered especially if adequate samples of the transitions are costly or time consuming to obtain. So, it is necessary to further consider more general jump systems with partial information on transition probabilities. The concept for MJS with partially unknown transition probabilities is first proposed in [8] and a series of studies have been carried out [9–12] recently. A new approach for the analysis and synthesis for Markov jump linear systems with incomplete transition descriptions has been proposed in [12], which can be further used for other analysis and synthesis issues, such as the stability of Markovian jump singular systems (MJSS).
A lot of attention has already been focused on robust stability, robust stabilization, and control problems for MJSS in recent years, such as the works in [13–17]. However, to the best of the authors’ knowledge, the necessary and sufficient conditions for the stochastic stability and stabilization problems of MJSS have not been fully investigated, especially when the transition probabilities are partially known. The authors in [15, 16] have, respectively, studied the problems of stability and stabilization for a class of continuoustime (discretetime) singular hybrid systems. New sufficient and necessary conditions for these singular hybrid systems to be regular, impulsefree (causal), and stochastically stable have been proposed in terms of a set of coupled strict linear matrix inequalities (LMIs). But the case of systems with partly known transition probabilities still needs to be considered. In addition to this, it is important to mention that the derivation of strict LMIs for MJSS with incomplete transition probabilities renders the synthesis of the state feedback controllers easier. These problems are important and challenging in both theory and practice, which motivates us for this study.
In this paper, the problem of the stability and stabilization of MJSS with partly known transition probabilities is addressed. Inspired by the ideas in [12], which fully unitized the properties of the transition rate matrix (TRM) and the convexity of the uncertain domains, we explore a new sufficient and necessary condition in terms of strict linear matrix inequalities (LMIs) for the MJSS to be regular, impulsive, and stochastically stable. Then, based on the proposed stability criterion, the conditions for state feedback controller are derived. Finally, numerical examples are given to illustrate the effectiveness of the proposed method.
Compared with the existing works about the stability and stabilization of Markovian jump systems, the current paper has the following novel features. First, the current paper deals with the stability and stabilization problems for MJSS with partly known transition probabilities, while most literatures (e.g., [8–12]) focused on those of normal ones that are special cases of MJSS. Second, the conservatism in the conventional studies [15] is eliminated by considering the fact that the unknown elements of each row in TRM exist. Moreover, the difficulty that the unknown elements contain diagonal elements is also overcome by introducing a lower bound of the diagonal element without additional conservatism.
Notation. The notation used in this technical note is standard. The superscript “” stands for matrix transposition; denotes the dimensional Euclidean space; represents the sets of positive integers, respectively. For the notation , represents the sample space, is the algebra of subsets of the sample space, and is the probability measure on . stands for the mathematical expectation. In addition, in symmetric block matrices or long matrix expressions, we use as an ellipsis for the terms that are introduced by symmetry and stands for a blockdiagonal matrix constituted by . The notation means is real symmetric positive definite, and is adopted to denote for brevity. and represent, respectively, identity matrix and zero matrix. Matrices, if their dimensions are not explicitly stated, are assumed to be compatible for algebraic operations.
2. Preliminaries and Problem Formulation
Consider the following continuoustime MJSS with Markovian jump parameters: where is the state vector and is the control input. The matrix is supposed to be singular with . The stochastic process taking values in a finite set is described by a continuoustime, discretestate homogeneous Markov process and has the following mode transition probabilities: where , , and denotes the switching rate from mode at time to mode at time , and for all . The TRM is given by
The set contains modes of system (1) and for , the system matrices of the ith mode are denoted by , , which are known realvalued constant matrices of appropriate dimensions that describe the nominal system.
The transition rates described above are considered to be partially available; that is, some elements in matrix are unknown. Take system (1) with 4 operation modes for example; the TRM may be written as where “” denotes the unknown element.
For , we denote
If , is further described as where represents the index of the th known element in the th row of matrix . Also, throughout the technical note, we denote When is unknown, it is necessary to provide a lower bound for it and .
Now, we introduce the following definition for the continuoustime MJSS (1) (with ).
Definition 1 (see [17]). (i)The continuoustime MJSS in (1) is said to be regular if, for each , is not identically zero.(ii)The continuoustime MJSS in (1) is said to be impulsive if, for each , .(iii)The continuoustime MJSS in (1) is said to be stochastically stable if, for any and , there exists a scalar such that where is the mathematical expectation, and denotes the solution to system (1) at time under the initial conditions and .(iv)The continuoustime MJSS in (1) is said to be stochastically admissible if it is regular, impulsive, and stochastically stable.
The following lemma is recalled, which will be used in what follows.
Lemma 2 (see [18]). Let be symmetric such that , , and are nonsingular. Then, is nonsingular and its inverse is expressed as where and are full column rank with , , and satisfies and , respectively. is symmetric and is nonsingular such that
3. Main Results
In this section, we will derive the stochastic stability criteria for system (1) when the transition probabilities are partially unknown and design a statefeedback controller and a static output feedback controller such that the closedloop system is stochastically stabilizable. The modedependent controller considered here has the form where are the controller gains to be determined. The closedloop systems obtained by applying controllers (11) to system (1) are
First, we provide the following lemma which presents a necessary and sufficient condition for the continuoustime MJSS with completely known transition probabilities matrix to be stochastically admissible.
Lemma 3 (see [15]). System (1) with is stochastically admissible if and only if there exist matrices , , and , such that the following coupled LMIs hold for each :
Let us first give the stability result for the unforced system (1) (with ). The following theorem presents a necessary and sufficient condition on the stochastic admissibility of the considered system with partially unknown transition probabilities.
Theorem 4. Consider the unforced system (1) with partially unknown transition probabilities. The corresponding system is stochastically admissible if and only if there exist matrices and nonsingular symmetric matrices , such that for each where and is a given lower bound for the unknown diagonal element.
Proof. Consider two cases, and , and note that system (1) is stochastically stable if and only if (13) holds.
Case 1 . It should be noted that in this case one has . We only need to consider since means the elements in the th row of the TRM are known, so it is not considered here. Now the lefthand side of (13) in Lemma 3 can be rewritten as
where the elements are unknown. Since and , we know that
Therefore, for , is equivalent to , which implies that, in the presence of unknown elements , the system stochastic admissibility is ensured if and only if (14) holds.
Case 2 . In this case, is unknown, , and . We also only consider since ; then the th row of the TRM is completely known.
Now the lefthand side of (15) can be rewritten as
Likewise, since we have and , we know that
which means that is equivalent to ,
As is lower bounded by , we have
which implies that
for some arbitrarily small. Then can be further written as a convex combination
where takes value arbitrarily in . Thus, (14) holds if and only if ,
simultaneously hold. Since is arbitrarily small, (24) holds if and only if
which is the case in (25) when . Hence (20) is equivalent to (15).
Therefore, we can conclude that the unforced system (1) with unknown elements in the TRM is stochastically admissible if and only if (14) and (15) hold for and , respectively.
Remark 5. Theorem 4 presents a new necessary and sufficient condition of stochastic admissibility criterion for the MJSS (1). The approach adopted in Theorem 4, which uses the TRM property (the sum of each row is zero), has extended the result of Theorem 1 in [12] to the MJSS. Note that the lower bound, , of is allowed to be arbitrarily negative.
Now let us consider the stabilization problem of system (1) in the presence of unknown elements in the TRM. The following theorem presents a condition for the existence of a modedependent stabilizing controller of the form in (11).
Theorem 6. Let be given scalars. Consider the closedloop system (12) with partially unknown transition probabilities. If there exist matrices and nonsingular matrices , matrices and such that, for each , the following LMIs hold:where
Then there exists a modedependent stabilizing controller of the form in (11) such that the closedloop system is stochastically admissible. The gain of the stabilizing state feedback controller is given by
Proof. Consider the closedloop system (12) and replace by in (14) and (15), respectively. Then, if , by Schur complement and performing a congruence transformation to (14) by , with , we can obtainLet and ; we have
So (31) becomes
Considering the nonlinear term in the above inequalities, the following inequalities are introduced. For any scalars , , by Lemma 2, the following inequalities hold:
Note that ; we have
So (33) holds if (27) is fulfilled. In a similar way, if , (28) can be worked out from (15). Therefore, the closedloop system is stochastically admissible, and the desired controller gain is given by (30).
Remark 7. It should be pointed out that if the diagonal elements in the TRM contain unknown ones, the system admissibility, the existence of the admissible controller, and the controller gains solution will be dependent on . The conditions of Theorem 6 are strict LMIs; hence they can be easily tractable by Matlab LMI toolbox.
4. Examples
Example 1. Consider system (1) with four operation modes and the following system matrices:
The transition rate matrix is given as shown in Table 1.

Let , and denote the unknown elements. Using Theorem 6 and the LMI control toolbox of Matlab, we obtain the controller gains for the system as follows: The closedloop dynamic responses and the Markovian chain are shown in Figure 1 with the initial condition .
(a)
(b)
Example 2. Consider system (1) with three operation modes and the following system matrices:
The transition rate matrix is given as shown in Table 2.

Let . In the 2nd row of TRM, the diagonal element is unknown; we assign its lower bound a priori with different values . Using Theorem 6 and LMI control toolbox in Matlab, the controller gains for the system are given by
When , we obtain the controller gains differently for the system as follows: It is seen from above that the obtained controller gains are dependent on . The closedloop dynamic responses and the Markovian chain are shown in Figure 2 with the initial condition and .
(a)
(b)
Remark 8. Notice that, in Example 1, all the diagonal elements of TRM are known and, in Example 2, there are unknown diagonal elements in the TRM which illustrate that the controller design is dependent on the lower bound of the corresponding unknown diagonal element. So they cannot be solved by the stabilization criterions developed in [15] which lack considering the case of systems with partly known transition probabilities. Moreover, here examples are for MJSS, while the stabilization criterions developed in [12] which focused on those of normal ones that are special cases of MJSS.
5. Conclusion
The problems of stability and state feedback control for continuoustime MJSS with partly known transition probabilities have been studied. A new sufficient and necessary condition for this class of system to be stochastically admissible has been proposed in terms of strict LMIs. Furthermore, sufficient conditions for the state feedback controller are derived, and numerical examples have also been given to illustrate the main results. However, the study of stability and stabilization of continuoustime MJSS with partly known transition probabilities is a basic problem which only serves as a stepping stone to investigate more complicated systems. However, timedelay appears commonly in various practical systems, and researchers have been paying remarkable attention to the problems of analysis and synthesis for timedelay systems [18–24]. The approaches proposed in this paper could be further extended to timedelay systems in our future work. It is expected that the approach can be further used for other analysis and synthesis issues such as analysis, synthesis, and other applications such as Markov jumping neural networks with incomplete transition descriptions.
Conflict of Interests
The authors declare that there is no conflict of interests regarding the publication of this paper.
Acknowledgment
This work is supported by Postdoctoral Research Sponsorship in Henan Province (Grant no. 2013013).
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Copyright © 2014 Jumei Wei and Rui Ma. This 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.