Research Article | Open Access

# Improved Results on Reachable Set Bounding for Linear Delayed Systems with Polytopic Uncertainties

**Academic Editor:**Luca Guerrini

#### Abstract

This paper focuses on bound of reachable sets for delayed linear systems with polytopic uncertainties. Based on Lyapunov-Krasovskii functional theory, delay decomposition technique, and reciprocally convex method, some new results expressed in the form of linear matrix inequalities are derived. It should be noted that triple integral functionals are first to be introduced for reachable set analysis. Consequently, a tighter bound of the reachable set is obtained. Four numerical examples are given to illustrate the effectiveness and advantage of the proposed results comparing with the existing criteria.

#### 1. Introduction

In real world, many phenomena can be described by time delay systems, such as communication networks, biology, and physical process. It is well known that the presence of time delay may lead to complicated behaviors for dynamic system, including instability, oscillations, and robustness [1–5]. In addition to stability and robustness of the state, the property of input-to-state for dynamical systems is also concerned. For a dynamic system, reachable set is the set of all the states in the Euclidean space that are reachable from the origin, in finite time, by inputs with peak value that is bounded by some given positive scalar [6]. It was first considered in the late 1960s and it has a wide range of applications, such as peak-to-peak gain minimization problem and control systems with actuator saturation. Thus, the problem of reachable set bounding for time delay systems has received considerable attention in recent years; for instance, see [6–18] and the references therein.

There are already some relevant results about the problem of reachable set bounding for linear systems. An LMI condition for an ellipsoid that bounded the reachable set of linear systems without time delay was given by Boyd in [13]. In [6], Fridman and Shaked firstly derived LMIs criteria of an ellipsoid that bounded the reachable set of uncertain systems with time-varying delays and bounded peak input based on the Razumikhin theory. In [11], Kim proposed an improved condition by using the modified Lyapunov-Razumikhin functionals. Nam and Pathirana obtained a smaller reachable set bound by the delay decomposition technique [10]. The maximal Lyapunov functionals, combined with the Razumikhin method, were employed to give a nonellipsoidal description of the reachable set in [15]. More recently, the authors derived the ellipsoid bounds of reachable sets of linear uncertain linear discrete-time systems based on the idea to minimize the projection distances of the ellipsoids on each axis with different exponential convergence rates [17]. Based on property of Metzler matrix, a new approach which did not involve the Lyapunov-Krasovskii functional method was used to get the state bounding for linear time-delayed systems [18]. The delays considered in [6–9, 11, 12, 15, 16, 18] are from 0 to an upper bound. However, delays may vary in an interval for which lower bound of delays is not necessary to be 0, such as [10]. On the other hand, the authors considered nondifferentiable time-varying delays in [10, 18], and differentiable time-varying delays were considered in [6–9, 11, 12, 15, 16]. Paper [11] assumed the derivative of delay to be less than 1. As is well known, large value of derivative of delay may yield bigger reachable set bounding. These constraints on the delays are strong and may be relaxed.

In this paper, we study the reachable set bounding for linear delayed systems with polytopic uncertainties. Constraints for delay are relaxed. Time delays vary in an interval for which lower bound of delays is not necessarily 0, and value of derivative of delay is not necessarily less than 1. Inspired by the Lyapunov functionals in [2], we construct Lyapunov-Krasovskii functionals, combining with the delay decomposition technique and reciprocally convex method to derive a more accurate description of the reachable set bound. Different from the Lyapunov functionals in [2], the integral terms of Lyapunov functionals in this paper contain . Moreover, to the best of our knowledge, it is first time to introduce triple integral functionals for reachable set analysis. We will show that the reachable set bound is tighter than that of [6, 8–12, 14]. Numerical examples illustrate the effectiveness and improvement of the obtained results.

*Notations. *The notations are used in this paper except where otherwise specified. is the -dimension Euclidean space and denotes the set of -dimension real matrices; real matrix means that is a symmetric positive definite (positive semidefinite) matrix. Superscript “” denotes transposition of a vector or a matrix; represents the elements below the main diagonal of a symmetric block matrix; denotes an identity matrix; “—” in tables represents no feasible solution for linear matrix inequality.

#### 2. Preliminaries

Consider the following uncertain polytopic time-delayed linear systems with disturbances:where is the state vector; is the disturbance. One has , , and . , , are known constant matrices. is the time-varying delay. For disturbance , we assume that , where is a constant.

The uncertainties are expressed as a linear convex-hull of known matrices , , and : with and .

In this paper, time-varying delay will be considered in two cases:(a),(b), .

The following lemmas are useful in deriving the criteria.

Lemma 1 (see [19]). *The following relation is known as the Leibniz rule:*

Lemma 2 (see [4]). *For any constant matrix and such that the following integrations are well defined, then*

Lemma 3 (see [5]). *For any constant matrix , scalars such that the following integrations are well defined, then *

*Proof. *By using Lemma 2, one can obtain According to Schur complement, the following inequality holds: Integrating both sides of the above inequality from to , we haveBy using Schur complement again, inequality (8) is equivalent to the inequality in Lemma 3. This completes the proof.

Lemma 4 (see [1]). *Let : have positive values in an open subset of . Then, the reciprocally convex combination of over satisfies **subject to *

Lemma 5 (see [2]). *For any vectors , , constant matrices (), , and scalars , satisfying , then following inequality holds: **subject to *

Lemma 6 (see [13]). *Let be a Lyapunov function for system (1) with . If **then .*

#### 3. Main Results

In order to study the reachable set bounding of uncertain system (1), firstly, we consider , , in system (1); that is,

The reachable set bounding of system (14) with time-varying delay for case (a) and case (b) is stated in Theorems 7 and 8, respectively.

After that, the reachable set bounding of uncertain system (1) with time-varying delay for case (a) and case (b) is stated in Theorems 9 and 10, respectively.

Theorem 7. *If there exist matrices , , , , , , , , and , , with appropriate dimensions, and a scalar , such that the following inequalities hold,**where **then the reachable sets of system (14) are bounded by a ball with*

*Proof. *Construct the following Lyapunov-Krasovskii functional, where Taking the time derivative of along the trajectory of system (14), we obtainUsing Lemma 2,where , , and .

Using Lemma 3,From Lemma 5 and inequality (15), one can obtainObviously, the following equation holds:Combining (20)–(24), one getswhere Since (15) hold, we can conclude that .

Therefore, one can obtain by Lemma 6.

Using the spectral properties of symmetric positive definite matrix , the following inequality holds:This further implies that due to (27). This completes the proof.

If the derivative of time delay is known, that is, the case , bounding for reachable set of system (14) is described in the following.

Theorem 8. *If there exist matrices , , , , , , , , , and , , with appropriate dimensions, and a scalar , such that the following inequalities hold,**where**then the reachable sets of system (14) are bounded by a ball with*

*Proof. *We modify the Lyapunov-Krasovskii functional where Taking the time derivative of along the trajectory of system (14),By the same way as in proof Theorem 7, one can obtain the result easily. The proof is completed.

Next, consider the linear system with polytopic uncertainties (1). Bounding for reachable set of system (1) with the case is obtained in Theorem 9.

Theorem 9. *If there exist matrices , , , , , , , , and , , with appropriate dimensions, and a scalar , satisfying the following inequalities for all ,**where **then the reachable sets of system (1) are bounded by a ball with*

*Proof. *Replacing with , , and in proof of Theorem 7, respectively, we easily get the conclusion.

If the derivative of time delay is known, that is, the case , bounding for reachable set of uncertain system (1) is obtained in Theorem 10.

Theorem 10. *If there exist matrices , , , , , , , , , and , , with appropriate dimensions, and a scalar , satisfying the following inequalities for all ,**where **then the reachable sets of system (1) are bounded by a ball with*

*Proof. *Replacing , , with , , and in Theorem 8, respectively, one can easily obtain the conclusion.

*Remark 11. *In [7, 8, 12], the constraint of was . is considered in this paper; it is more general than [7, 8, 12].

*Remark 12. *In this paper, delay decomposition technique and reciprocally convex method are used to construct Lyapunov functionals, and triple integral terms are introduced in Lyapunov functionals for the first time to investigate bounds of reachable set for systems with uncertainties, which may lead to tighter bounding for reachable set.

*Remark 13. *In order to guarantee negative definite, is required that in [11]. It should be noted that the value of derivative of time delay is not necessarily less than 1 in Theorems 8 and 10 since the term − can be negative definite by choosing appropriate when . Obviously, the results in this paper have more scope of application than the one in [11].

*Remark 14. *The reachable set of system (1) can be minimized by solving the following optimization problem for a scalar :

*Remark 15. *It is clear to see that radius is smallest if is largest in inequalities (35) and (39). Therefore, one can consider () as decision variable with the additional requirement (42).

*Remark 16. *It should be noted that the matrix inequalities in Theorems 7–10 cannot be simplified to LMIs. However, when is fixed, then the matrix inequalities reduce to LMIs. Hence, we can use MATLAB’s Toolbox to solve the matrix inequalities in Theorems 7–10.

*Remark 17. *The approach is likely to help further work in this area. It may be used to improve estimate partial state bounding for neural networks with time-varying delays, such as [3].

#### 4. Examples

In this section, four numerical examples will be presented to show the validity of the main results derived in this paper.

*Example 1. *Consider the following uncertain time-delayed system with parameters:

By solving optimization problems (42), computed ’s for the case with different values of are listed in Table 1. Computed 's for different values of with and for the case are obtained in Tables 2 and 3, respectively. It is clear to see that the proposed method in this paper yields tighter bounds than literatures [6, 8, 11].