Complexity

Volume 2019, Article ID 6048909, 7 pages

https://doi.org/10.1155/2019/6048909

## A Generalization of the Cauchy-Schwarz Inequality and Its Application to Stability Analysis of Nonlinear Impulsive Control Systems

^{1}School of Statistics, Southwestern University of Finance and Economics, Chengdu 611130, China^{2}School of Mathematics and Statistics, Chongqing Technology and Business University, Chongqing 400067, China^{3}Key Laboratory of Intelligent Information Processing and Control, Chongqing Three Gorges University, Chongqing 404100, China

Correspondence should be addressed to Yang Peng; moc.361@1102gnay_gnep

Received 8 October 2018; Revised 19 January 2019; Accepted 12 February 2019; Published 7 March 2019

Academic Editor: Giacomo Innocenti

Copyright © 2019 Yang Peng 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

In this paper, we first present a generalization of the Cauchy-Schwarz inequality. As an application of our result, we obtain a new sufficient condition for the stability of a class of nonlinear impulsive control systems. We end up this note with a numerical example which shows the effectiveness of our method.

#### 1. Introduction

In this paper, the Euclidean norm of is defined as . We use and to denote the largest and the smallest eigenvalues of a real square matrix with real eigenvalues, respectively. Let be a spectral decomposition with is orthogonal. Then the functional calculus for is defined as where is a continuous real-valued function defined on a real interval and is a real symmetrical matrix with eigenvalues in [1].

During the last three decades, many people have studied impulsive control method because it is an efficient way in dealing with the stability of complex systems [2–4]. For example, impulsive control method can be used in the synchronization and stabilization of chaos systems [5–11] and neural network systems [12–23].

In this paper, we consider a class of nonlinear impulsive control systems as follows:where is the state variable and is output, and , , are constant matrices. The nonlinear part is a continuous function which satisfies and . If , then there will be a jump in the system and , with and . Without loss of generality, we assume that For simplicity, we can rewrite this last system asThe stability problems of nonlinear impulsive control system (5) have been investigated extensively in the literature in the past several decades. For example, a number of sufficient conditions for the stability of nonlinear impulsive control system (5) are derived in [24–27]. Inequalities play an important role in their research, for instance, by using the Cauchy-Schwarz inequality [1] and comparison principle [27], and Yang showed a sufficient condition for the stability of nonlinear impulsive control system (5). For more results on applications of the Cauchy-Schwarz inequality to impulsive control theory, the reader is referred to [4] and the references therein.

In this paper, we first present a generalization of the Cauchy-Schwarz inequality by using some results of matrix analysis and techniques of inequalities. As an application of our result, we obtain a new sufficient condition for the stability of nonlinear impulsive control system (5). We end up this note with a numerical example which will show the effectiveness of our result.

#### 2. A Generalization of the Cauchy-Schwarz Inequality

In this section, we will give a generalized Cauchy-Schwarz inequality.

Lemma 1. *Let be positive definite and suppose that are the largest and the smallest eigenvalues of , respectively. If satisfyfor a certain , thenwhere*

*Proof. *First we assume that . Let and then, we have andSmall calculations show that and are the eigenvalues of . Suppose that are the largest and the smallest eigenvalues of , respectively. Then we haveandIt follows from (12) and (13) that It can easily be seen that the function is decreasing and so which is equivalent towhere Note that and It follows thatMeanwhile, by the Cauchy-Schwarz inequality, we haveOn the other hand, the arithmetic-geometric mean inequality for scalars implies thatIt follows from (21), (22), and (23) thatBy using inequalities (17) and (24), we obtainNow we consider the general situation. For arbitrary , we have By inequality (25), we havewhere Inequality (6) implies that and so Small calculations show that the function is decreasing and soIt follows from (27) and (31) that This completes the proof of our result.

*Remark 2. *By the Cauchy-Schwarz inequality, we know that condition (6) holds for any if we choose . And so Lemma 1 is a generalization of the Cauchy-Schwarz inequality:

*Remark 3. *If is orthogonal, then we can choose and Lemma 1 is the well-known Wielandt inequality:

#### 3. An Application of Lemma 1

Let us recall the definition of the angle between two vectors : In the course of experiment, we note that for some systems the state variable and nonlinear part have special relationships. For instance, Lü et al. [28] presented the following chaotic system: where . Note that , and so . That is, they are orthogonal. So we want to know whether the angle between and has an effect on the stability of systems. And the results showed in [24–27] do not take into account this factor. This is the motivation for the present paper.

In this section, as an application of Lemma 1, we present a new sufficient condition for the stability of nonlinear impulsive control system (5). Compared with Theorem 3 in [27] (see also Theorem 3.1.5 in [4]), if we consider the angle factor, then we will get a larger stable region for some systems.

Lemma 4 (see [1]). *Suppose that is a real symmetrical matrix and let be the largest and smallest eigenvalues of , respectively. Thenfor any .*

Theorem 5. *Let be positive definite and suppose that are the largest and smallest eigenvalues of , respectively. Let be the largest eigenvalue of with . Suppose that is the largest eigenvalue of . Iffor a certain andwherethen the origin of nonlinear impulsive control system (5) is asymptotically stable.*

*Proof. *Let For , we haveBy Lemma 4 and noting that the matrices and have the same eigenvalues, we obtainBy Lemmas 1 and 4 and , we haveIt follows from (43), (44), and (45) that For , by using Lemma 4 again and noting that the matrices and have the same eigenvalues, we obtain To avoid repetition, we omit the following proof because it is same as that of Theorem 3 in [27]. This completes the proof of our result.

*Remark 6. *If we choose , then by the Cauchy-Schwarz inequality we know that inequality (38) holds for any and condition of (40) becomes which is the condition of Theorem 3 in [27] (see also [4]). So, our result is a generalization of Theorem 3 in [27].

*Remark 7. *If , condition of (40) will be replaced by

*Remark 8. *Let us discuss Lü’s [28] chaotic system again. Noting that and taking into consideration that we can choose , then inequality (38) holds and condition of (40) becomesFurthermore, if we choose , then this last condition can be simplified as which contains the condition of Theorem 3.2.1 in [4] (see also [10]).

*Remark 9. *Lemma 1 has some other applications in impulsive control theory; for example, by using Lemma 1 and comparison lemmas on the sufficient condition for the stability of nonlinear impulsive differential systems shown in [24–26], some results presented in [24–26] can be generalized.

#### 4. A Numerical Example

We end up this paper with a numerical example which shows the effectiveness of our method.

In 2005, Qi and Chen et al. [29] produced a new system which is described by where This system is chaotic whenBy definition of the Euclidean norm, we have By Figure 1, we know that , so we can choose . By the arithmetic-geometric mean inequality for scalars we know that So, we can choose . In this example, we choose the matrices as follows: Simple calculations show that and so we have We choose , and then Putting the simulation results are shown in Figure 2.