 Research
 Open Access
Finitetime stability of linear nonautonomous systems with timevarying delays
 Xueyan Yang^{1} and
 Xiaodi Li^{1, 2}Email authorView ORCID ID profile
https://doi.org/10.1186/s1366201815573
© The Author(s) 2018
 Received: 12 February 2018
 Accepted: 12 March 2018
 Published: 22 March 2018
Abstract
In this paper, we investigate the problem of finitetime stability (FTS) of linear nonautonomous systems with timevarying delays. By constructing an appropriated function, we derive some explicit conditions in terms of matrix inequalities ensuring that the state trajectories of the system do not exceed a certain threshold over a prespecified finite time interval. Finally, two examples are given to show the effectiveness of the main results.
Keywords
 Finitetime stability
 Linear nonautonomous systems
 Timevarying delay
 Metzler matrix
1 Introduction
During the past decades, finite time stability (FTS) in linear systems has received considerable attention since it was first introduced in 1960s. FTS is a system property which concerns the quantitative behavior of the state variables over an assigned finitetime interval. A system is FTS if, given a bound on the initial condition, its state trajectories do not exceed a certain threshold during a prespecified time interval. Hence, FTS enables us to specify quantitative bounds on the state of a linear system and plays an important role in addressing transient performances of the systems. Therefore, in recent years, many interesting results for FTS have been proposed, see [1–5] for instances. It should be noticed that FTS and Lyapunov asymptotic stability (LAS) are completely independent concepts. Indeed, a system can be FTS but not LAS, and vice versa [6–8]. Asymptotic stability in dynamical systems implies convergence of the system trajectories to an equilibrium state over the infinite horizon. However, in practice, it is desirable that a dynamical system possesses FTS, that is, its state norm does not exceed a certain threshold in finite time. Furthermore, LAS is concerned with the qualitative behavior of a system and it does not involve quantitative information (e.g., specific estimates of trajectory bounds), whereas FTS involves specific quantitative information.
In the process of investigating linear systems, time delays are frequently encountered [9–12]. And in hardware implementation, time delays usually cause oscillation, instability, divergence, chaos, or other bad performances of neural networks. In recent years, various interesting results have been obtained for the FTS of linear autonomous systems. For linear timeinvariant systems with constant delay, some finitetime stability conditions have been derived in terms of feasible linear matrix inequalities based on the Lyapunov–Krasovskii functional methods [6, 13–17]. It is worth noting that nonautonomous phenomena often occur in many realistic systems; for instance, when considering a longterm dynamical behavior of the system, the parameters of the system usually change along with time [18–22]. Moreover, stability analysis for nonautonomous systems usually requires specific and quite different tools from the autonomous ones (systems with constant coefficients). To our knowledge, there are a few results concerned with the FTS of nonautonomous systems with timevarying delays. In addition, it should be noted that the conditions for FTS of the timevarying system are usually based on the Lyapunov or Riccati matrix differential equation [7, 23, 24], which leads to indefinite matrix inequalities and lacks effective computational tools to solve them. Therefore, when dealing with the FTS of timevarying systems with delays, an alternative approach is clearly needed, which motivates our present investigation.
In present paper, the problems of FTS are investigated for linear nonautonomous systems with discrete and distributed timevarying delays. By constructing an appropriated function, some sufficient conditions are derived to guarantee the FTS of the addressed linear nonautonomous systems. We do not impose any restriction on the states of the system in this sense, which is better than the results in [25]. The rest of this paper is organized as follows. In Sect. 2, some notations, definitions, and a lemma are given. In Sect. 3, we present the main results. Two examples are provided in Sect. 4 to demonstrate the effectiveness of the proposed criteria. Section 5 shows the summary of this paper.
2 Preliminaries
Notations
Let \(\mathbb{R}\) denote the set of real numbers, \(\mathbb{R}_{+}\) the set of positive numbers, \(\mathbb{R}^{n}\) the ndimensional real spaces equipped with the norm \(\x\_{\infty}=\max_{i\in\underline{n}}x_{i}\) and \(\mathbb{R}^{n\times m}\) the \(n\times m\)dimensional real spaces. I denotes the identity matrix with appropriate dimensions and \(\Lambda=\{1,2,\ldots,n\}\). For any interval \(J\subseteq\mathbb {R}\), set \(S\subseteq\mathbb{R}^{k}\) (\(1\leq k\leq n\)), \(C(J, S) =\{\varphi:J\rightarrow S\mbox{ is continuous}\}\). \(\mathscr{F}=\{\mu:\mathbb{R}_{+}\rightarrow\mathbb{R}_{+}\mbox{ is continuously differentiable}\}\) and \(A\vee B=\max\{A, B\}\) for constants A and B. \(u=(u_{i})\), \(v=(v_{i})\) in \(\mathbb{R}^{n}\), \(u\geq v\) iff \(u_{i}\geq v_{i}\), \(\forall i\in\Lambda\); \(u\gg v\) iff \(u_{i}>v_{i}\), \(i\in\Lambda\).
Definition 1
(Amato et al. [7])
Definition 2
(Liao et al. [26])
 (A_{1}):

\(a_{ii}(t)\leq\bar{a}_{ii}\), \(i\in\Lambda\), \(a_{ij}(t)\leq \bar{a}_{ij}\), \(i\neq j\), \(i,j\in\Lambda\), \(t\in[0,T]\).
 (A_{2}):

\(d_{ij}(t)\leq\bar{d}_{ij}\), \(g_{ij}(t)\leq\bar{g}_{ij}\), \(i,j\in\Lambda\), \(t\in[0,T]\).
Lemma 1
(Hien et al. [29])
 (i)
A is a nonsingular Mmatrix.
 (ii)
\(\operatorname{Re}\lambda_{k}(A)>0\) for all eigenvalues \(\lambda_{k}(A)\) of A.
 (iii)
There exist a matrix \(B\geq0\) and a scalar \(s>\rho(B)\) such that \(A=sI_{n}B\), where \(\rho(B)=\max\{\lambda_{k}(A)\}\) denotes the spectral radius of B.
 (iv)
There exist a vector \(\xi\in\mathbb{R}^{n}\) and \(\xi\gg0\) such that \(A\xi\gg0\).
 (v)
There exist a vector \(\eta\in\mathbb{R}^{n}\) and \(\eta\gg0\) such that \(A^{T}\eta\gg0\).
3 Main results
We are now in a position to state our main result as follows. In this section, we shall investigate the FTS of the linear nonautonomous system by constructing an appropriated function and using the Metzler matrix method.
Theorem 1
Proof
Corollary 1
Corollary 2
Remark 1
In [25], Hien considered the FTS of system (1) and derived some conditions for exponential estimation. In this paper, we study the FTS of system (1) via the auxiliary function μ, and some new sufficient conditions for FTS, which are different from the results in [25], are derived. In other words, our development result is more general than the result in [25].
4 Example
In this section, we present two numerical examples to illustrate the effectiveness of the proposed results.
Example 1
Case I. Let us take \(r_{1}=1\), \(r_{2}=1.25\), and then system (8) is FTS with respect to \((T, r_{1}, r_{2})\) for any finite time \(0< T\leq T_{\mathrm{max}}=25\), and in this case \(\beta_{3}=0.08\). Note that in [25], the maximum value of T is \(T_{\mathrm{max}}=21.3144\). Hence, our result is more general than [25].
Case II. Let us take \(r_{1}=1\), when \(T=21.3144\), we obtain \(\beta _{3}=0.8243\) and \(r_{2}=1.213144\).
Example 2
5 Conclusion
In the present paper, we have investigated the FTS of a class of nonautonomous systems with timevarying delays. Some new sufficient conditions for FTS have been derived in terms of inequalities for a type of Metzler matrixes. Finally, two examples were provided to show the effectiveness of the proposed method.
Declarations
Acknowledgements
This work was supported by the National Natural Science Foundation of China (11301308, 61673247), and the Research Fund for Distinguished Young Scholars and Excellent Young Scholars of Shandong Province (JQ201719, ZR2016JL024). The paper has not been presented at any conference.
Authors’ contributions
The main idea of this paper was proposed by YX and LX. YX prepared the manuscript initially and performed all the steps of the proofs in this research. All authors read and approved the final manuscript.
Competing interests
The authors declare that none of the authors have any competing interests in the manuscript.
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Authors’ Affiliations
References
 Stojanovic, S.: Further improvement in delaydependent finitetime stability criteria for uncertain continuoustime systems with timevarying delays. IET Control Theory Appl. 10, 926–938 (2016) MathSciNetView ArticleGoogle Scholar
 Amato, F., Carannante, G., Tommasi, G., Pironti, A.: Input–output finitetime stability of linear systems: necessary and sufficient conditions. IEEE Trans. Autom. Control 57, 3051–3063 (2012) MathSciNetView ArticleMATHGoogle Scholar
 Pang, D., Jiang, W.: Finitetime stability analysis of fractional singular timedelay systems. Adv. Differ. Equ. 2014(1), 259 (2014) MathSciNetView ArticleMATHGoogle Scholar
 Yang, W., Sun, J.: Finitetime stability of quantum systems with impulses. IET Control Theory Appl. 8, 641–646 (2014) MathSciNetView ArticleGoogle Scholar
 Kang, W., Zhong, S., Shi, K., Cheng, J.: Finitetime stability for discretetime system with timevarying delay and nonlinear perturbations. ISA Trans. 60, 67–73 (2016) View ArticleGoogle Scholar
 Amato, F., Ariola, M., Cosentino, C.: Finitetime stabilization via dynamic output feedback. Automatica 42, 337–342 (2006) MathSciNetView ArticleMATHGoogle Scholar
 Amato, F., Ambrosino, R., Ariola, M., Cosentino, C.: Finitetime stability of linear timevarying systems with jumps. Automatica 45, 1354–1358 (2009) MathSciNetView ArticleMATHGoogle Scholar
 Lin, X., Du, H., Li, S., Zou, Y.: Finitetime boundedness and finitetime \(l_{2}\) gain analysis of discretetime switched linear systems with average dwell time. J. Franklin Inst. 350, 911–928 (2013) MathSciNetView ArticleMATHGoogle Scholar
 Li, X., Wu, J.: Stability of nonlinear differential systems with statedependent delayed impulses. Automatica 64, 63–69 (2016) MathSciNetView ArticleMATHGoogle Scholar
 Rakkiyappan, R., Udhayakumar, K., Velmurugan, G., et al.: Stability and Hopf bifurcation analysis of fractionalorder complexvalued neural networks with time delays. Adv. Differ. Equ. 2017(1), 225 (2017) MathSciNetView ArticleGoogle Scholar
 Li, X., Song, S.: Stabilization of delay systems: delaydependent impulsive control. IEEE Trans. Autom. Control 62, 406–411 (2017) MathSciNetView ArticleMATHGoogle Scholar
 Li, X., Cao, J.: An impulsive delay inequality involving unbounded timevarying delay and applications. IEEE Trans. Autom. Control 62, 3618–3625 (2017) MathSciNetView ArticleMATHGoogle Scholar
 He, S., Liu, F.: Observerbased finitetime control of timedelayed jump systems. Appl. Math. Comput. 217, 2327–2338 (2010) MathSciNetMATHGoogle Scholar
 Xiang, W., Xiao, J., Iqbal, M.: Robust finitetime bounded observer design for a class of uncertain nonlinear Markovian jump systems. IMA J. Math. Control Inf. 29, 551–572 (2012) MathSciNetView ArticleMATHGoogle Scholar
 Zhang, Y., Liu, C., Mu, X.: Robust finitetime stabilization of uncertain singular Markovian jump systems. Appl. Math. Model. 36, 5109–5121 (2012) MathSciNetView ArticleMATHGoogle Scholar
 Lin, X., Du, H., Li, S., Zou, Y.: Finitetime boundedness and finitetime \(l_{2}\) gain analysis of discretetime switched linear systems with average dwell time. J. Franklin Inst. 350, 911–928 (2013) MathSciNetView ArticleMATHGoogle Scholar
 Hou, L., Zong, G., Wu, Y.: Observerbased finitetime exponential \(l_{2}\)\(l_{\infty}\) control for discretetime switched delay systems with uncertainties. Trans. Inst. Meas. Control 35, 310–320 (2013) View ArticleGoogle Scholar
 Stamova, I., Stamov, T., Li, X.: Global exponential stability of a class of impulsive cellular neural networks with supremums. Int. J. Adapt. Control Signal Process. 28(11), 1227–1239 (2014) MathSciNetView ArticleMATHGoogle Scholar
 Li, X., Song, S.: Impulsive control for existence, uniqueness and global stability of periodic solutions of recurrent neural networks with discrete and continuously distributed delays. IEEE Trans. Neural Netw. Learn. Syst. 24, 868–877 (2013) View ArticleGoogle Scholar
 Wang, X., Jiang, M., Fang, S.: Stability analysis in Lagrange sense for a nonautonomous Cohen–Grossberg neural network with mixed delays. Nonlinear Anal., Theory Methods Appl. 70, 4294–4306 (2009) MathSciNetView ArticleMATHGoogle Scholar
 Li, X., Bohner, M., Wang, C.: Impulsive differential equations: periodic solutions and applications. Automatica 52, 173–178 (2015) MathSciNetView ArticleMATHGoogle Scholar
 Thuan, M., Hien, L., Phat, V.: New results on exponential stabilization of timevarying delay neural networks via Riccati equations. Appl. Math. Comput. 246, 533–545 (2014) MathSciNetMATHGoogle Scholar
 Yang, X., Li, X.: Robust finitetime stability of singular nonlinear systems with interval timevarying delay. J. Franklin Inst. 355, 1241–1258 (2018) MathSciNetView ArticleGoogle Scholar
 Amato, F., Ariola, M., Cosentino, C.: Finitetime control of discretetime linear systems: analysis and design conditions. Automatica 46, 919–924 (2010) MathSciNetView ArticleMATHGoogle Scholar
 Hien, L.: An explicit criterion for finitetime stability of linear nonautonomous systems with delays. Appl. Math. Lett. 30, 12–18 (2014) MathSciNetView ArticleMATHGoogle Scholar
 Liao, X., Wang, L., Yu, P.: Stability of Dynamical Systems. Elsevier, Amsterdam (2007) View ArticleMATHGoogle Scholar
 Ngoc, P.: On exponential stability of nonlinear differential systems with timevarying delay. Appl. Math. Lett. 25, 1208–1213 (2012) MathSciNetView ArticleMATHGoogle Scholar
 Berman, A., Plemmons, R.: Nonnegative Matrices in the Mathematical Sciences. SIAM, Philadelphia (1987) MATHGoogle Scholar
 Hien, L., Son, D.: Finitetime stability of a class of nonautonomous neural networks with heterogeneous proportional delays. Appl. Math. Comput. 251, 14–23 (2015) MathSciNetMATHGoogle Scholar