- About this Journal ·
- Abstracting and Indexing ·
- Aims and Scope ·
- Annual Issues ·
- Article Processing Charges ·
- Articles in Press ·
- Author Guidelines ·
- Bibliographic Information ·
- Citations to this Journal ·
- Contact Information ·
- Editorial Board ·
- Editorial Workflow ·
- Free eTOC Alerts ·
- Publication Ethics ·
- Reviewers Acknowledgment ·
- Submit a Manuscript ·
- Subscription Information ·
- Table of Contents

Mathematical Problems in Engineering

Volume 2013 (2013), Article ID 586091, 15 pages

http://dx.doi.org/10.1155/2013/586091

## Nonfragile Guaranteed Cost Control and Optimization for Interconnected Systems of Neutral Type

^{1}Key Laboratory of Manufacturing Industrial Integrated Automation, Shenyang University, Shenyang 110044, China^{2}Department of Fundamental Teaching, Shenyang Institute of Engineering, Shenyang 110136, China^{3}Institute of Systems Science, Northeastern University, Shenyang, Liaoning 110004, China

Received 4 April 2013; Accepted 19 June 2013

Academic Editor: Ming Cao

Copyright © 2013 Heli Hu et al. 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.

#### Abstract

The design and optimization problems of the nonfragile guaranteed cost control are investigated for a class of interconnected systems of neutral type. A novel scheme, viewing the interconnections with time-varying delays as effective information but not disturbances, is developed to decrease the conservatism. Many techniques on decomposing and magnifying the matrices are utilized to obtain the guaranteed cost of the considered system. Also, an algorithm is proposed to solve the nonlinear problem of the interconnected matrices. Based on this algorithm, the minimization of the guaranteed cost of the considered system is obtained by optimization. Further, the state feedback control is extended to the case in which the underlying system is dependent on uncertain parameters. Finally, two numerical examples are given to illustrate the proposed method, and some comparisons are made to show the advantages of the schemes of dealing with the interconnections.

#### 1. Introduction

Time delays often arise in the processing state, input or related variables of dynamic systems. Particularly, when the state derivative also contains time delay, the considered systems are called neutral systems [1]. The outstanding characteristic of neutral systems is the fact that such systems contain the same highest order derivatives for the state vector , at both time and past time(s) . Many engineering systems can be represented as neutral equation [2–10], such as heat exchangers, robots in contact with rigid environments [11], distributed networks containing lossless transmission lines [12], and population ecology [13]. Therefore, great interest has been devoted to analysis and synthesis of a class of neutral delay systems. The delay-dependent stability criteria for stochastic systems of neutral type are studied in [3, 6]. The difference between them is that the exponential stability problem is investigated in the former, and the robust stochastic stability, stabilization, and control problems are considered in the other. Furthermore, the improved stability criteria for neutral systems are established by the method of a memory state feedback control [2] and by the method of a robust reduced order filter in [4]. In the context of infinite-dimensional linear systems modeled by neutral functional differential equations, a periodic output feedback is studied in [14] and the stabilization of neutral systems with delayed control is the main work. As the further results, in [15–17], the stability and performance analysis, the finite-time control, and the reliable stabilization for uncertain switched systems of neutral type are investigated, respectively.

On the other hand, interconnected systems appear in a variety of engineering applications including power systems, large structures and manufacturing systems, and their applications, such as [18–21]. In [18], Mukaidani investigates the stability of a class of nonlinear large-scale systems and proposes a suboptimal guaranteed cost control instead of solving the nonconvex optimization problem. But the time delays are invariant and not involved in the interconnections. Furthermore, the scheme of counteracting the interconnections to simplify the problem may add conservatism in some cases. In [19], Mahmoud and Xia propose a generalized approach to stabilization of systems which are composed of linear time delay subsystems coupled by linear time-varying interconnections. The decentralized structure of dissipative state-feedback controllers is designed to render the closed-loop interconnected system delay-dependent asymptotically stable with strict dissipativity. However, the optimization problem for the dissipativity is not taken into account. In [20], a decentralized control scheme for a class of linear large-scale interconnected systems with norm-bounded time-varying parameter uncertainties is designed under a class of control failures. It is worth noting that the considered systems do not include any time delay, and the optimization problem for the guaranteed cost is not investigated.

To the best of the authors’ knowledge, the nonfragile guaranteed cost control and optimization for neutral interconnected systems have not yet been investigated, which motivates the present study. One contribution of this paper is that a novel scheme, viewing the interconnections with time-varying delays as effective information but not disturbances, is developed to decrease the conservatism. The other contribution lies in the fact that an algorithm is proposed to solve the nonlinear constraint problem caused by the interconnected matrices. In this paper, the designed control is the state feedback control with gain perturbations. Also, the guaranteed cost of the considered system can be obtained by solving the corresponding matrix inequality. Based on the proposed algorithm, the minimization of the guaranteed cost of the considered system can be obtained by optimization. particuraly, the matrix is introduced to denote the square root matrix of symmetric positive semidefinite matrix , that is, with the eigenvector matrix of satisfying and the diagonal eigenvalues matrix of .

The remainder of the paper is organized as follows. The nonfragile control problem formulation is described in Section 2. In Section 3, the guaranteed cost control with gain perturbations and optimization are investigated for unperturbed and uncertain neutral interconnected systems. The numerical examples, the simulation results, and some comparisons are presented in Section 4. The conclusion is provided in Section 5.

#### 2. Problem Formulation

Consider the following uncertain neutral interconnected systems composed of subsystems: where and are the state vector and the input vector of the th subsystem, respectively. is the interconnections between the th subsystem and the other subsystems, in which is known interconnected matrices of appropriate dimensions, and implies the interconnections between the th subsystem and the other subsystems have different time-varying delays , , . , , , , and are known constant matrices of appropriate dimensions. is the initial condition. Assume that there exist constants , , , , , , , , and satisfying Time-varying parametric uncertainties , , , , and are assumed to be of the following form: where , , , , , and are constant matrices of appropriate dimensions, and is the unknown matrix function satisfying , for all .

Construct the following state feedback control with gain perturbations: where is the control gain to be designed, and is a perturbed matrix satisfying , where and are known matrices of appropriate dimensions, and satisfies , for all ; the resulting closed-loop uncertain neutral interconnected systems are obtained:

Define the following quadratic cost function: where and are two given symmetric positive definite matrices.

One objective of this paper is to design a control (4) and determine a scalar satisfying the following two conditions:(a)the closed-loop system (5) is asymptotically stable,(b).

If the aforementioned control gain and constant exist, control (4) is the decentralized nonfragile guaranteed cost control and is the guaranteed cost for the considered system.

The other is to find out , the minimization of the guaranteed cost .

Lemma 1 (see [8]). *Let , , , and be matrices of appropriate dimensions. Assuming that is symmetric and , then if and only if there exists a scalar satisfying
*

Lemma 2 (see [8]). *For any constant matrix and differentiable vector function with appropriate dimensions, one has
*

#### 3. Main Result

##### 3.1. Nonfragile Guaranteed Cost Control and Optimization for Unperturbed Neutral Interconnected Systems

For convenience, firstly consider the following unperturbed neutral interconnected systems with time-varying delays:

Now a sufficient condition for existence of the decentralized nonfragile guaranteed cost control (4) for unperturbed neutral interconnected systems (9) with cost function (6) is presented in the following results.

Theorem 3. *Assume . If there exist a positive number , some symmetric positive definite matrices , and matrix such that the following inequality holds:
**
then control (4) with is the decentralized nonfragile guaranteed cost control of unperturbed neutral interconnected systems (9) with the following guaranteed cost:
**
where*

*Proof. *Choose , , and , and construct the following Lyapunov functional:

Obviously, for all . Differentiating along the trajectories of the unperturbed neutral interconnected systems (9) with control (4) and applying (2) and Lemma 2 yield

According to Lemma 1 and the following the fact:
one can obtain

Therefore, it follows from (14) and (16) that
where

Define
where , .

Pre- and postmultiplying the matrix in (19) by and , where , the following matrix is obtained:
where

Define
where

The following equality is obvious:
where

By Lemma 1 and Schur complement formula, the condition in (10) is equivalent to in (24). By Schur complement formula with , one can obtain in (20). The condition is equivalent to . Again, by Schur complement formula with , one can obtain . From the condition in (17), there exists a constant , such that

By conditions (13) and (26) and , one can conclude that system (9) with (2) and (4) is asymptotically stable. From (17) with , one can obtain

Therefore, the following equalities hold:

This completes the proof.

*Remark 4. *It is obvious that for every subsystem, the corresponding in (10) is an LMI with obtained matrices and in the last inequality (i.e., the inequality ). Hence, the decentralized nonfragile control (4) and the guaranteed cost in (11) can be obtained by finding feasible set to with in [22] one by one.

*Remark 5. *Obviously, the guaranteed cost in (11) cannot be directly optimized by using the toolbox of in [22]. One reason is that inequalities (10) with variable matrices and are not a group of LMIs but coupled nonlinear inequalities. Another reason is that is a nonconvex function with respect to the optimization variables.

The following algorithm is given to solve the nonlinear problem of inequalities (10).

*Algorithm 6. *Choose constant matrices and satisfying in , where , .

It is needed to simultaneously select constant matrices and satisfying . For simplicity, one can choose and to be positive definite diagonal matrices according to the eigenvalues of due to . The chosen entries need to be as small as possible, because
is involved in . However, if there is no solution to inequalities (10), the large scalars can be considered.

In the sequel, instead of solving the nonconvex optimization problem, a suboptimal method of minimizing the guaranteed cost , based on Algorithm 6, is presented.

Theorem 7. *Consider unperturbed system (9) with cost function (6), and assume . If the following optimization problem:
**
subject to LMI (10) with Algorithm 6, and
**
has a solution set , , , , , , where , , , , , then control (4) with is the decentralized nonfragile guaranteed cost control of unperturbed system (9) with the minimization of the guaranteed cost as follows:
*

*Proof. *Applying the Schur complement formula to LMIs (31) leads to , , , respectively.

Noting that [8]
the following inequalities are obtained

Further, one can obtain

Therefore, it follows from (11) that

The minimization of the right hand of inequality (36) implies the minimization of the guaranteed cost for unperturbed system (9). This completes the proof.

##### 3.2. Nonfragile Guaranteed Cost Control for Uncertain Neutral Interconnected Systems

Theorem 8. *Consider uncertain neutral interconnected systems (1) with (2), (3), and (4). If there exist positive numbers , , and , some symmetric positive definite matrices , , , and matrix such that the following inequalities hold:
**
then control (4) with is the decentralized nonfragile guaranteed cost control of uncertain neutral interconnected systems (1) with the guaranteed cost in (11), where ,
*

*Proof. *From condition (10) with unperturbed neutral interconnected systems (9), one can obtain the corresponding condition to stabilize uncertain neutral interconnected systems (1) as follows:
where .

By Lemma 1 and Schur complement formula, the condition in (37) is equivalent to in (40). For the same reason, (38) is equivalent to

This implies that uncertain neutral interconnected systems (1) are Lipschitz in the term with Lipschitz constant less than [8]. By the same derivation of Theorem 3, one can complete this proof.

The decentralized nonfragile guaranteed cost control (4) and the minimization of the guaranteed cost for uncertain neutral interconnected systems (1) are determined by the following theorem.

Theorem 9. *Consider uncertain neutral interconnected systems (1) with (2), (3), (4), and cost function (6). If the following optimization problem:
**
is subject to LMI (37) with Algorithm 6, (38), and (31) has a solution set , , , , , , , , then control (4) with is the decentralized nonfragile guaranteed cost control for uncertain neutral interconnected systems (1) with the minimization of the guaranteed cost in (32). *

*Remark 10. *Reference [18] develops a scheme of counteracting the interconnections to simplify the problem, which may add conservatism in some cases. Compared with the approach of treating the interconnections in [18], we utilize an approach of magnifying the terms associated interconnections; for details, one can see the derivation of inequality (16). To some extent, it may reduce the conservatism of the results derived in the paper.

#### 4. Illustrative Examples

In this section, some examples are presented to show the validity of the control approach and the advantages of the schemes of dealing with the interconnections.

*Example 1. *To illustrate the design method of the decentralized nonfragile guaranteed cost control and the optimization approach of the guaranteed cost for uncertain neutral interconnected system, consider uncertain neutral interconnected systems (1) composed of two third-order subsystems: