Research Article | Open Access

Zhenbo Li, Shuqian Zhu, "Dynamic Output Feedback Stabilization of Controlled Positive Discrete-Time Systems with Delays", *Discrete Dynamics in Nature and Society*, vol. 2010, Article ID 640806, 12 pages, 2010. https://doi.org/10.1155/2010/640806

# Dynamic Output Feedback Stabilization of Controlled Positive Discrete-Time Systems with Delays

**Academic Editor:**Binggen Zhang

#### Abstract

The problem of stabilization by means of dynamic output feedback is studied for discrete-time delayed systems with possible interval uncertainties. The control is under positivity constraint, which means that the resultant closed-loop system must be stable and positive. The robust resilient controller is respect to additive controller gain variation which also belongs to an interval. Necessary and sufficient/sufficient conditions are established for the existence of the dynamic output feedback controller. The desired controller gain matrices can be determined effectively via the cone complementarity linearization techniques.

#### 1. Introduction

A dynamical system is called positive if any trajectory of the system starting from nonnegative initial states remains forever nonnegative. Such systems abound in almost all fields, for instance, engineering, ecology, economics, biomedicine, and social science [1–3]. Since the states of positive systems are confined within a “cone" located in the positive quadrant rather than in linear spaces, many well-established results for general linear systems cannot be readily applied to positive systems. This feature makes the analysis and synthesis of positive systems a challenging and interesting job, and many results have been obtained, see [4–12]. It should be pointed out that in [9–12], the governed system is not necessarily positive, while a control strategy can be designed such that the closed-loop system is positive. We call systems in this class controlled positive.

The reaction of real world systems to exogenous signals is never instantaneous and always infected by certain time delays. The delay presence may induce complex behaviors, such as oscillations, instability, and poor performance [13]. Recently, the study on delayed positive systems has drawn increasing attention and many important results have been obtained, see [14–17] for stability and [18–20] for control. It has been shown that the stability of delayed positive systems has nothing to do with the amplitude of delays.

It should be noted that in most literature aforementioned for delayed systems, it is assumed that the parameters of systems are exactly known, and the controller takes the form of state feedback. However, in practical applications, it is inevitable that uncertainties enter the system parameters and it is often impossible to obtain the full information on the state variables. Hence, it is necessary to investigate the output feedback stabilization problem of uncertain positive system with delays. On the other hand, in practice, instead of being precise or exactly implemented, many controllers do have a certain degree of errors and may be sensitive to these errors. Such controllers are often termed “fragile". Therefore, it is considered beneficial to design a “resilient" controller being capable of tolerating some level of controller gain variations [21, 22]. All of the above motivate our research.

This paper deals with the dynamic output feedback stabilization problem for discrete-time delayed systems (not necessarily positive) under the positivity constraint, which means that the closed-loop systems are not only stable, but also positive. First, a new necessary and sufficient condition is given for the stability of discrete-time positive systems with delays, which is more useful for designing output feedback controllers. Then for systems with/without uncertainties, necessary and sufficient/sufficient conditions for the existence of the dynamic output feedback controllers are established in terms of linear matrix inequalities (LMIs) together with a matrix equality constraint. The controller gain matrices can be determined via the cone complementarity linearization techniques.

*Notations*

, , and denote the reals, the -dimensional linear vector space over the reals the nonnegative quadrant of , respectively. denotes the set of all real matrices. means that the elements of are nonnegative (nonpositive). For matrices , the notation or means that . stands for a symmetric positive (negative) definite matrix . The symbol denotes the spectral radius of matrix , that is, with being the spectrum of . The superscript represents the transpose. The symbol will be used in some matrix expressions to induce a symmetric structure.

#### 2. Mathematical Preliminaries

In this section, we will give some definitions and lemmas about positive discrete-time delayed systems.

Consider the discrete-time system with delay where is the state, is the measurable output, , and are known real constant matrices, is a constant delay and is the vector valued initial function.

First, some definitions and lemmas are given.

*Definition 2.1 (see [17]). *System (2.1) is said to be positive if for any , one has and for all .

Lemma 2.2 (see [17]). *System (2.1) is positive if and only if , and .*

*Definition 2.3 (see [15]). *A square matrix is called a Schur matrix if .

Lemma 2.4 (see [15]). *Positive system (2.1) is asymptotically stable if and only if is a Schur matrix.*

Lemma 2.5 (see [3]). *A matrix is a Schur matrix if and only if there exists a diagonal matrix such that *

Combining the above lemmas, we have

Lemma 2.6. *Positive system (2.1) is asymptotically stable if and only if there exists a diagonal matrix such that
*

Lemma 2.7 (see [2]). *For two matrices if then .*

#### 3. Dynamic Output Feedback Stabilization

Now consider the discrete-time system with delay and control where , , are, respectively, the state, the control input and the measurable output. and are known constant matrices, is a constant delay and is the vector valued initial function.

The purpose of this section is to design a dynamic output feedback controller such that the resultant closed-loop system is positive and asymptotically stable. Where is the state of the controller, , and are the controller gain matrices to be determined. The above stabilization problem will be called Problem DOFS (Dynamic Output Feedback Stabilization).

*Remark 3.1. * may be either equal to or less than . In the case of or controller (3.2) is called the full-order or reduced-order dynamic output feedback controller for system (3.1), respectively.

First, similar to [8], in order to design dynamic output feedback controller for system (3.1), we give an equivalent form of Lemma 2.6.

Theorem 3.2. *Positive system (2.1) is asymptotically stable if and only if there exist diagonal matrices and satisfying the LMI
**
and the matrix equality constraint
*

*Proof. *By Schur complement, it is easy to see that (2.2) holds if and only if the following inequality holds:
Taking , we have that there exist a diagonal matrix satisfying (2.2) if and only if there exist diagonal matrices and satisfying (3.4) and (3.5).

*Remark 3.3. *Comparing with Lemma 2.6, the conditions in Theorem 3.2 are a little more complicated since a matrix equality constraint is introduced. However, it can be seen that in Theorem 3.2, the Lyapunov matrix and the system parametric matrices have been decoupled. Hence, Theorem 3.2 is more useful when designing output feedback controllers for system (3.1).

Based on Theorem 3.2, we will establish the necessary and sufficient conditions for the solvability of Problem DOFS.

Theorem 3.4. *For discrete-time delayed system (3.1) with , , there exists a solution to Problem DOFS if and only if there exist matrices , and diagonal matrices , , satisfying the LMIs
**
and the matrix equality constraints
**
In this case, the controller gain matrices in (3.2) are designed as
*

#### 4. Robust Resilient Stabilization of Interval Systems

In this section, we consider the discrete-time interval uncertain system (3.1), where the system parametric matrices are all uncertain with and are known constant matrices.

For uncertain system (3.1), we will design a resilient dynamic output feedback controller such that the resultant closed-loop system with is positive and robustly stable. In (4.2), is the state of the controller, , , , and are the controller gain matrices to be determined and , , , and are the controller gain variations which are assumed to satisfy with , , , , , , , being known constant matrices.

In the sequel, the above stabilization problem will be stated as Problem RRDOFS (Robust Resilient Dynamic Output Feedback Stabilization).

*Assumption. *Assume that

*Remark 4.2. *In fact, Assumption 4.1 is without loss of generality. For example, for any matrices and with , let , , then can be rewritten as
where is the new controller gain matrix to be determined and is the new controller gain variation which satisfies . Similarly, we can prove the generality of the assumption on , and .

Next, we will establish the sufficient conditions for the solvability of Problem RRDOFS.

Theorem 4.3. *For the interval uncertain delayed system (3.1) with the parametric matrices satisfying (4.1), there exists a solution to Problem RRDOFS if there exist matrices , , , and diagonal matrices , , satisfying the following LMIs: **and the matrix equality constraints (3.8). In this case, the controller gain matrices in (4.2) are designed as
*

*Proof. *Letting
with , , , , , and noting that (4.1) and (4.5)-(4.6), we get From (4.8a)–(4.8d), (4.9)–(4.11a), (4.11b), (4.11c), (4.11d), and (4.11e) and , , , , , we obtain that , , for all uncertainties and . By using Lemma 2.2, we conclude that the closed-loop system (4.3) is positive.

Noting (4.8e) and by using Lemma 2.4 and Theorem 3.2, we have that is a Schur matrix considering (4.9). From (4.11e) and Lemma 2.7, we get is also a Schur matrix for all uncertainties. Hence, the positive system (4.3) is robustly stable.

*Remark 4.4. *From the proof of Theorem 4.3, we can see that the condition in (4.1) that , is given for the purpose to find the upper bound and the lower bound about the parametric matrices of the closed-loop system (4.3).

If in system (3.2), there are no uncertainties in the parametric matrices and , that is, and are known constant matrices, we will obtain the the necessary and sufficient conditions for the solvability of Problem RRDOFS, which will be given as follows.

Theorem 4.5. *For the interval uncertain delayed system (3.1) with (4.1) and and being known constant matrices, there exists a solution to Problem RRDOFS if there exist matrices , and diagonal matrices , , satisfying the following LMIs: **and the matrix equality constraints (3.8). In this case, the controller gain matrices in (4.2) are designed as
*

*Proof. *The sufficiency can be easily obtained from Theorem 4.3 by letting , and , .

Now we will prove the necessity. Suppose that for the interval uncertain delayed system (3.1) with (4.1) and and being known constant matrices, Problem RRDOFS is solvable, that is, there exists matrices and such that the closed-loop (4.3) is positive and asymptotically stable for any , and , then we have that both the systemsare positive and asymptotically stable.

From , we obtain that system (4.14a) is positive if and only if
Thus (4.12a)–(4.12d) hold considering (4.13).

From the positivity and stability of system (4.14b) and using Theorem 3.2 again, we conclude that there exist matrices , and diagonal matrices , , satisfying (3.8) and (4.12e). The necessity is proved.

*Remark 4.6. *We stress out that the conditions in above theorems do not impose the restriction on the governed system that the system matrix . That is, the free system is not necessarily positive. Therefore, the governed system considered in this paper is called controlled positive system.

*Remark 4.7. *The matrix equality constraint in the above theorems can be solved via the cone complementarity linearization techniques [8].

#### 5. Numerical Examples

*Example 5.1. *Consider the discrete-time delayed system (3.1) with

It is easy to see that is not nonnegative, which implies that the unforced system (3.1) is not positive. By solving the conditions in Theorem 3.4, after 1 iteration, we obtain the full-order DOFS controller gain matrices
and the reduced-order DOFS controller gain matrices

*Example 5.2. *Consider the uncertain discrete-time delayed system (3.1) with

It is easy to see that is nonnegative while is not, which implies that the unforced system (3.1) is not always positive within the set of uncertain system matrices. And computation shows that the eigenvalues of are 0.1853, 1.0147. From Lemma 2.4, we know that the unforced system (3.1) is not always asymptotically stable.

Solving the conditions in Theorem 4.3 gives the RRDOFS controller gain matrices
after 1 iteration.

#### 6. Conclusions and Future Works

In this paper, we have studied the dynamic output feedback stabilization problem for delayed systems with/wihtout interval uncertainties. The controller/resilient controller which has additive controller gain variation belonging to an interval, is designed to guarantee that the resulting closed-loop systems are not only stable, but also positive. Necessary and sufficient/sufficient conditions for the existence of such controllers are established in terms of linear matrix inequalities together with a matrix equality constraint. And the controller gain matrices can be determined via the cone complementarity linearization techniques. The approach presented in this paper can also solve the corresponding problems for continuous-time delayed systems.

#### Acknowledgments

This work was supported by National Natural Science Foundation of P. R. China (50977054, 61004011) and Science Research Foundation of Shandong Economic University.

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#### Copyright

Copyright © 2010 Zhenbo Li and Shuqian Zhu. 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.