Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Cluster lag synchronization of delayed heterogeneous complex dynamical networks via intermittent pinning control

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

This paper investigates the problem of cluster lag synchronization in the heterogeneous dynamical networks by using an intermittent pinning control strategy. Previous related works mainly focused on the time-varying delays in the self-dynamics, which was not consistent with the real world. The transmission delay in the communication channels is considered in this paper. We present several criteria to guarantee cluster lag synchronization without assuming the coupling matrix being symmetric and irreducible. A decentralized adaptive intermittent pinning control scheme is employed to reduce the control cost. An effective pinned-cluster selection scheme is adopted to guide what kind of clusters should be pinned preferentially. Two simulations are proposed to verify the correctness of the theoretical results.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Strogatz SH (2001) Exploring complex networks. Nature 410(6825):268–276

    Article  MATH  Google Scholar 

  2. Newman MEJ (2003) The structure and function of complex networks. SIAM Rev 45(2):167–256

    Article  MathSciNet  MATH  Google Scholar 

  3. Wang J, Wu H (2012) Local and global exponential output synchronization of complex delayed dynamical networks. Nonlinear Dyn 67(1):497–504

    Article  MathSciNet  MATH  Google Scholar 

  4. Ning D, Wu X, Lu J et al (2015) Driving-based generalized synchronization in two-layer networks via pinning control. Chaos 25(11):113104

    Article  MathSciNet  MATH  Google Scholar 

  5. Li H, Liao X, Chen G et al (2015) Event-triggered asynchronous intermittent communication strategy for synchronization in complex dynamical networks. Neural Netw 66(C):1–10

    MATH  Google Scholar 

  6. Li H, Liao X, Huang T et al (2015) Second-order global consensus in multiagent networks with random directional link failure. IEEE Trans Neural Netw Learn Syst 26(3):565–575

    Article  MathSciNet  Google Scholar 

  7. Cai G, Jiang S, Cai S et al (2015) Cluster synchronization of overlapping uncertain complex networks with time-varying impulse disturbances. Nonlinear Dyn 80(1–2):503–513

    Article  MathSciNet  MATH  Google Scholar 

  8. Yang S, Li C, Huang T (2017) Synchronization of coupled memristive chaotic circuits via state-dependent impulsive control. Nonlinear Dyn 88(1):115–129

    Article  MATH  Google Scholar 

  9. Cheng J, Park J et al (2018) An asynchronous operation approach to event-triggered control for fuzzy markovian jump systems with general switching policies. IEEE Trans Fuzzy Syst 26(1):6–18

    Article  Google Scholar 

  10. Lai H, Huang Y (2015) Chaotic secure communication based on synchronization control of chaotic pilot signal. Computational intelligence and intelligent systems. Springer, Singapore

    Google Scholar 

  11. Prakash M, Balasubramaniam P, Lakshmanan S (2016) Synchronization of markovian jumping inertial neural networks and its applications in image encryption. Neural Netw 83:86–93

    Article  Google Scholar 

  12. Aghababa MP, Aghababa HP (2013) Robust synchronization of a chaotic mechanical system with nonlinearities in control inputs. Nonlinear Dyn 73(1–2):363–376

    Article  MathSciNet  MATH  Google Scholar 

  13. Zhang J, Ma Z, Zhang G (2013) Cluster synchronization induced by one-node clusters in networks with asymmetric negative couplings. Chaos 23(4):043128

    Article  MathSciNet  MATH  Google Scholar 

  14. Zhou L, Wang C, Du S et al (2017) Cluster synchronization on multiple nonlinearly coupled dynamical subnetworks of complex networks with nonidentical nodes. IEEE Trans Neural Netw Learn Syst 28(3):1–14

    Article  MathSciNet  Google Scholar 

  15. Li H, Liao X, Chen G et al (2017) Attraction region seeking for power grids. IEEE Trans Circuits Syst II Express Briefs 64(2):201–205

    Article  Google Scholar 

  16. Huang T, Li C, Duan S et al (2012) Robust exponential stability of uncertain delayed neural networks with stochastic perturbation and impulse effects. IEEE Trans Neural Netw Learn Syst 23(6):866–875

    Article  Google Scholar 

  17. Zeng Z, Huang T, Zheng W (2010) Multistability of recurrent neural networks with time-varying delays and the piecewise linear activation function. IEEE Trans Neural Netw Learn Syst 21(8):1371–1377

    Article  Google Scholar 

  18. Huang T, Li C, Yu W et al (2009) Synchronization of delayed chaotic systems with parameter mismatches by using intermittent linear state feedback. Nonlinearity 22(3):569–584

    Article  MathSciNet  MATH  Google Scholar 

  19. Chen T, Liu X, Lu W (2007) Pinning complex networks by a single controller. IEEE Trans Circuits Syst I Regular Papers 54(6):1317–1326

    Article  MathSciNet  MATH  Google Scholar 

  20. Yu W, Chen G, Lü J et al (2013) Synchronization via pinning control on general complex networks. SIAM J Control Optim 51(2):1395–1416

    Article  MathSciNet  MATH  Google Scholar 

  21. Shi L, Zhu H, Zhong S et al (2017) Cluster synchronization of linearly coupled complex networks via linear and adaptive feedback pinning controls. Nonlinear Dyn 88(2):859–870

    Article  MATH  Google Scholar 

  22. Liu X, Chen T (2015) Synchronization of linearly coupled networks with delays via aperiodically intermittent pinning control. IEEE Trans Neural Netw Learn Syst 26(10):2396–2407

    Article  MathSciNet  Google Scholar 

  23. Ali MS, Yogambigai J (2016) Exponential stability of semi-markovian switching complex dynamical networks with mixed time varying delays and impulse control. Neural Process Lett 46:1–21

    Google Scholar 

  24. Wang X, She K, Zhong S et al (2016) New result on synchronization of complex dynamical networks with time-varying coupling delay and sampled-data control. Neurocomputing 214:508–515

    Article  Google Scholar 

  25. Hu A, Cao J, Hu M et al (2015) Cluster synchronization of complex networks via event-triggered strategy under stochastic sampling. Phys A 434(15):99–110

    Article  MathSciNet  MATH  Google Scholar 

  26. Liu X, Chen T (2011) Cluster synchronization in directed networks via intermittent pinning control. IEEE Trans Neural Netw 22(7):1009–1020

    Article  Google Scholar 

  27. Mei J, Jiang M, Wu Z et al (2015) Periodically intermittent controlling for finite-time synchronization of complex dynamical networks. Nonlinear Dyn 79(1):295–305

    Article  MATH  Google Scholar 

  28. Cai S, Zhou P, Liu Z (2015) Intermittent pinning control for cluster synchronization of delayed heterogeneous dynamical networks. Nonlinear Anal Hybrid Syst 18:134–155

    Article  MathSciNet  MATH  Google Scholar 

  29. He W, Zhang S, Ge S (2014) Adaptive control of a flexible crane system with the boundary output constraint. IEEE Trans Ind Electron 61(8):4126–4133

    Article  Google Scholar 

  30. He W, Ge S (2015) Vibration control of a flexible beam with output constraint. IEEE Trans Ind Electron 62(8):5023–5030

    Article  Google Scholar 

  31. Zhao Z, He W, Yin Z et al (2017) Spatial trajectory tracking control of a fully actuated helicopter in known static environment. J Intell Robot Syst 85(1):1–18

    Article  Google Scholar 

  32. He W, Zhang S, Ge S (2014) Robust adaptive control of a thruster assisted position mooring system. Automatica 50(7):1843–1851

    Article  MathSciNet  MATH  Google Scholar 

  33. He W, Chen Y, Yin Z (2016) Adaptive neural network control of an uncertain robot with full-state constraints. IEEE Trans Cybern 46(3):620–629

    Article  Google Scholar 

  34. Zhang X, Ma H, Yang C (2017) Decentralised adaptive control of a class of hidden leader–follower non-linearly parameterised coupled mass. IET Control Theory A 11(17):3016–3025

    Article  MathSciNet  Google Scholar 

  35. Zhou P, Cai S (2017) Pinning synchronization of complex directed dynamical networks under decentralized adaptive strategy for aperiodically intermittent control. Nonlinear Dyn 90(1):287–299

    Article  MathSciNet  MATH  Google Scholar 

  36. Jiang S, Lu X (2016) Synchronization analysis of coloured delayed networks under decentralized pinning intermittent control. Pramana 86(6):1243–1251

    Article  Google Scholar 

  37. Wang K, Fu X, Li K (2009) Cluster synchronization in community networks with nonidentical nodes. Chaos 19(2):023106

    Article  MathSciNet  MATH  Google Scholar 

  38. Hu C, Jiang H (2012) Cluster synchronization for directed community networks via pinning partial schemes. Chaos Soliton Fractals 45(11):1368–1377

    Article  MathSciNet  MATH  Google Scholar 

  39. Su H, Rong Z, Chen MZQ et al (2013) Decentralized adaptive pinning control for cluster synchronization of complex dynamical networks. IEEE Trans Cybern 43(1):394–399

    Article  Google Scholar 

  40. Wang Y, Cao J (2013) Cluster synchronization in nonlinearly coupled delayed networks of non-identical dynamic systems. Nonlinear Anal Real 14(1):842–851

    Article  MathSciNet  MATH  Google Scholar 

  41. Xia W, Cao J (2009) Pinning synchronization of delayed dynamical networks via periodically intermittent control. Chaos 19(1):013120

    Article  MathSciNet  MATH  Google Scholar 

  42. Cai S, Zhou J, Xiang L et al (2008) Robust impulsive synchronization of complex delayed dynamical networks. Phys Lett A 372(30):4990–4995

    Article  MATH  Google Scholar 

  43. Horn RA, Johnson CR (1985) Matrix analysis. Cambridge University Press, Cambridge

    Book  MATH  Google Scholar 

  44. Song Q, Cao J (2010) On pinning synchronization of directed and undirected complex dynamical networks. IEEE Trans Circuits Syst I Regular Papers 57(3):672–680

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

The work described in this paper was supported in part by the Special Financial Support from China Postdoctoral Science Foundation under Grant 2017T100670, in part by the China Postdoctoral Science Foundation under Grant 2016M590852, in part by the Special Financial Support from Chongqing Postdoctoral Science Foundation under Grant Xm2017100, in part by the National Natural Science Foundation of China under Grants 61773321, 61762020 and 61503050, in part by the Science and Technology Foundation of Guizhou under Grants. QKHJC20161076 and QKHJC20181083, in part by the Science and Technology Top-notch Talents Support Project of Colleges and Universities in Guizhou under Grant QJHKY2016065 and in part by the High-level Innovative Talents Project of Guizhou under Grant QRLF201621.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Huaqing Li.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Appendices

Appendix 1

1.1 Proof of Theorem 1

Let \(E\left( t \right) = \left( {E_{1}^{\rm T} \left( t \right),E_{2}^{\rm T} \left( t \right), \ldots ,E_{m}^{\rm T} \left( t \right)} \right)^{\rm T}\), where \(E_{1} \left( t \right) = \left( {e_{1,1}^{\rm T} \left( t \right), \ldots ,e_{{r_{1} ,1}}^{\rm T} \left( t \right)} \right)^{\rm T}\), \(E_{2} \left( t \right) = \left( {e_{{r_{1} + 1,2}}^{\rm T} \left( t \right), \ldots ,e_{{r_{1} + r_{2} ,2}}^{\rm T} \left( t \right)} \right)^{\rm T} , \ldots .E_{m} \left( t \right) = \left( {e_{{r_{1} + r_{2} + \cdots + r_{m - 1} + 1,m}}^{\rm T} \left( t \right), \ldots ,e_{N,m}^{\rm T} \left( t \right)} \right)^{\rm T} .\)

Construct a Lyapunov candidate as follows, where \(\otimes\) is defined as Kronecker product.

$$\begin{aligned} W\left( t \right) & = \frac{1}{2}E^{\rm T} \left( t \right)\left( {I_{N} \otimes I_{n} } \right)E\left( t \right) = \frac{1}{2}\sum\nolimits_{k = 1}^{m} {E_{k}^{\rm T} \left( t \right)\left( {I_{{r_{k} }} \otimes I_{n} } \right)E_{k} \left( t \right)} \\ & = \frac{1}{2}\sum\nolimits_{k = 1}^{m} {\sum\nolimits_{{i \in C_{k} }} {e_{ik}^{\rm T} \left( t \right)e_{ik} \left( t \right)} } \\ \end{aligned}$$

When \(t \in \left[ {\omega T,\left( {\omega + \theta } \right)T} \right)\), \(\omega = 0,1,2, \ldots\), the time derivative of \(W\left( t \right)\) along the trajectory of (9) at time \(t\) can be given as

$$\begin{aligned} \dot{W}\left( t \right) & = \sum\limits_{k = 1}^{m} {\sum\limits_{{i \in C_{k} }} {e_{ik}^{\text{T}} \left( t \right)\tilde{f}_{k}^{{\tau_{k} ,\sigma }} \left( {t,x_{i} ,s_{k} } \right)} } + c\sum\limits_{k = 1}^{m} {\sum\limits_{{i \in C_{k} }} {\sum\limits_{p = 1}^{m} {\sum\limits_{{j \in C_{p} }} {b_{ij} e_{ik}^{\text{T}} \left( t \right)\varGamma e_{jp} \left( t \right)} } } } \\ & \quad - c\sum\limits_{k = 1}^{l} {E_{k}^{\text{T}} \left( t \right)\left( {d_{k} I_{{r_{k} }} \otimes \varGamma } \right)E_{k} \left( t \right)} . \\ \end{aligned}$$
(28)

In virtue of Assumption 2, we can get

$$\begin{aligned} & \sum\limits_{k = 1}^{m} {\sum\limits_{{i \in C_{k} }} {e_{ik}^{\text{T}} \left( t \right)\tilde{f}_{k}^{{\tau_{k} ,\sigma }} \left( {t,x_{i} ,s_{k} } \right)} } \\ & \quad \le \sum\limits_{k = 1}^{m} {\sum\limits_{{i \in C_{k} }} {L_{k}^{0} e_{ik}^{\text{T}} \left( t \right)\varGamma e_{ik} \left( t \right)} } + \sum\limits_{k = 1}^{m} {\sum\limits_{{i \in C_{k} }} {L_{k}^{\tau } e_{ik}^{\text{T}} \left( {t - \tau_{k} \left( t \right)} \right)\varGamma e_{ik} \left( {t - \tau_{k} \left( t \right)} \right)} } \\ & \quad = \sum\limits_{k = 1}^{m} {E_{k}^{\text{T}} \left( t \right)\left( {L_{k}^{0} I_{{r_{k} }} \otimes \varGamma } \right)E_{k} \left( t \right)} + \sum\limits_{k = 1}^{m} {E_{k}^{\text{T}} \left( {t - \tau_{k} \left( t \right)} \right)\left( {L_{k}^{\tau } I_{{r_{k} }} \otimes \varGamma } \right)E_{k} \left( {t - \tau_{k} \left( t \right)} \right)} . \\ \end{aligned}$$
(29)

Noticing that \(\sum\nolimits_{p = 1}^{m} {\sum\nolimits_{{i = C_{p} }} {b_{ij} } } = \sum\nolimits_{j = 1}^{N} {b_{ij} } = 0\), \(i = 1,2, \ldots ,N\) with \(\tilde{\varGamma } = \text{diag} \left( {\sqrt {\gamma_{1} } ,\sqrt {\gamma_{2} } , \ldots ,\sqrt {\gamma_{n} } } \right)\), one has

$$\begin{aligned} & \sum\limits_{k = 1}^{m} {\sum\limits_{{i = C_{k} }} {\sum\limits_{p = 1}^{m} {\sum\limits_{{j \in C_{p} }} {b_{ij} e_{ik}^{\text{T}} \left( t \right)\varGamma e_{jp} \left( t \right)} } } } = \sum\limits_{k = 1}^{m} {\sum\limits_{p = 1}^{m} {E_{k}^{\text{T}} \left( t \right)\left( {B_{kp} \otimes \varGamma } \right)E_{p} \left( t \right)} } \\ & \quad = \sum\limits_{k = 1}^{m} {E_{k}^{\text{T}} \left( t \right)\left( {B_{kk}^{s} \otimes \varGamma } \right)E_{k} \left( t \right)} + \sum\limits_{k = 1}^{m} {\sum\limits_{p = 1,p \ne k}^{m} {\sum\limits_{{i = C_{k} }} {\sum\limits_{{j \in C_{p} }} {b_{ij} \left( {\tilde{\varGamma }e_{ik} \left( t \right)} \right)^{\text{T}} \left( {\tilde{\varGamma }e_{jp} \left( t \right)} \right)} } } } \\ & \quad \le \sum\limits_{k = 1}^{m} {E_{k}^{\text{T}} \left( t \right)\left( {B_{kk}^{s} \otimes \varGamma } \right)E_{k} \left( t \right)} + \frac{1}{2}\sum\limits_{k = 1}^{m} {\sum\limits_{p = 1,p \ne k}^{m} {\sum\limits_{{i = C_{k} }} {\sum\limits_{{j \in C_{p} }} {b_{ij} \left( {e_{ik}^{\text{T}} \left( t \right)\varGamma e_{ik} \left( t \right) + e_{jp}^{\text{T}} \left( t \right)\varGamma e_{jp} \left( t \right)} \right)} } } } \\ & \quad = \sum\limits_{k = 1}^{m} {E_{k}^{\text{T}} \left( t \right)\left( {B_{kk}^{s} \otimes \varGamma } \right)E_{k} \left( t \right)} - \frac{1}{2}\sum\limits_{k = 1}^{m} {E_{k}^{\text{T}} \left( t \right)\left( {A_{k} \otimes \varGamma } \right)E_{k} \left( t \right)} + \frac{1}{2}\sum\limits_{k = 1}^{m} {E_{k}^{\text{T}} \left( t \right)\left( {\varPsi_{k} \otimes \varGamma } \right)E_{k} \left( t \right)} \\ & \quad = \sum\limits_{k = 1}^{m} {E_{k}^{\text{T}} \left( t \right)\left( {\left( {B_{kk}^{s} + \frac{1}{2}\varPsi_{k} - \frac{1}{2}A_{k} } \right) \otimes \varGamma } \right)E_{k} \left( t \right)} \\ \end{aligned}$$
(30)

Substituting (29) and (30) into (28), we get

$$\dot{W}\left( t \right) \le cE^{\rm T} \left( t \right)\left( {\left( {Z - D} \right) \otimes \varGamma } \right)E\left( t \right) + \frac{q}{2}\left( {\mathop {\sup }\limits_{t - \tau \le s \le t} \sum\limits_{k = 1}^{m} {E_{k}^{\text{T}} \left( s \right)E_{k} \left( s \right)} } \right).$$
(31)

According to condition \(\left( \text{i} \right)\) and the properties of the Kronecker product of matrix [42], from (31), one has

$$\begin{aligned} \dot{W}\left( t \right) & \le E^{\rm T} \left( t \right)\left( {\left( {cZ - cD + a_{1} I_{N} } \right) \otimes \varGamma } \right)E\left( t \right) - a_{1} E^{\rm T} \left( t \right)\left( {I_{N} \otimes \varGamma } \right)E\left( t \right) + q\left( {\mathop {\sup }\limits_{t - \tau \le s \le t} W\left( s \right)} \right) \\ & \le - 2a_{1} \lambda_{\hbox{min} } \left( \varGamma \right)W\left( t \right) + q\left( {\mathop {\sup }\limits_{t - \tau \le s \le t} W\left( s \right)} \right) = - p_{1} W\left( t \right) + q\left( {\mathop {\sup }\limits_{t - \tau \le s \le t} W\left( s \right)} \right). \\ \end{aligned}$$

Similarly, when \(t \in \left[ {\left( {\omega + \theta } \right)T,\left( {\omega + 1} \right)T} \right)\), \(\omega = 0,1,2, \ldots\), using condition \(\left( {\text{ii} } \right)\), we can deduce

$$\begin{aligned} \dot{W}\left( t \right) & \le E^{\rm T} \left( t \right)\left( {\left( {cZ - a_{2} I_{N} } \right) \otimes \varGamma } \right)E\left( t \right) + a_{2} E^{\rm T} \left( t \right)\left( {I_{N} \otimes \varGamma } \right)E\left( t \right) + q\left( {\mathop {\sup }\limits_{t - \tau \le s \le t} W\left( s \right)} \right) \\ & \le 2a_{2} \lambda_{\hbox{max} } \left( \varGamma \right)W\left( t \right) + q\left( {\mathop {\sup }\limits_{t - \tau \le s \le t} W\left( s \right)} \right) = p_{2} W\left( t \right) + q\left( {\mathop {\sup }\limits_{t - \tau \le s \le t} W\left( s \right)} \right). \\ \end{aligned}$$

As a result, we have

$$\left\{ {\begin{array}{ll} {\dot{W}\left( t \right) \le - p_{1} W\left( t \right) + q\left( {\mathop {\sup }\limits_{t - \tau \le s \le t} W\left( s \right)} \right),} & {\omega T \le t < \left( {\omega + \theta } \right)T,} \\ {\dot{W}\left( t \right) \le p_{2} W\left( t \right) + q\left( {\mathop {\sup }\limits_{t - \tau \le s \le t} W\left( s \right)} \right),} & {\left( {\omega + \theta } \right)T \le t < \left( {\omega + 1} \right)T.} \\ \end{array} } \right.$$
(32)

Next, we will prove that conditions (iii)–(iv) imply that, for \(t \ge 0\), we have \(\dot{W}\left( t \right) \le \left( {\mathop {\sup }\nolimits_{ - \tau \le s \le 0} W\left( s \right)} \right)e^{ - \varpi t}\).

Define \(\zeta \left( \lambda \right) = \lambda - p_{1} + qe^{\lambda \tau }\), where \(p_{1} > q > 0\) such that \(\zeta \left( 0 \right) < 0\), \(\zeta \left( { + \infty } \right) > 0\), \(\zeta^{\prime}\left( \lambda \right) > 0\). Utilizing the monotonicity and continuity of \(\zeta \left( \lambda \right)\), the equation \(\lambda - p_{1} + qe^{\lambda \tau } = 0\) has a unique positive solution \(\lambda > 0\). Let \(M_{0} = \mathop {\sup }\nolimits_{ - \tau \le s \le 0} W\left( s \right)\), \(V\left( t \right) = e^{\lambda t} W\left( t \right)\), \(t \ge 0\). Let \(S\left( t \right) = V\left( t \right) - hM_{0}\), where \(h > 1\) is a constant. Apparently, for any \(t \in \left[ { - \tau ,0} \right]\), one has

$$S\left( t \right) < 0.$$
(33)

Then, we indicate for all \(t \in \left[ {0,\theta T} \right]\), that

$$S\left( t \right) < 0.$$
(34)

From (33), there exist a \(t_{0} \in \left[ {0,\theta T} \right]\) such that

$$S\left( {t_{0} } \right) = 0,{\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} \dot{S}\left( {t_{0} } \right) \ge 0,$$
(35)
$$S\left( t \right) < 0,{\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} - \tau \le t < t_{0} .$$
(36)

By (32), (35) and (36), we have

$$\dot{S}\left( {t_{0} } \right) = \lambda V\left( {t_{0} } \right) + e^{{\lambda t_{0} }} \dot{W}\left( {t_{0} } \right) \le \left( {\lambda - p_{1} } \right)V\left( {t_{0} } \right) + qe^{{\lambda t_{0} }} \left( {\mathop {\sup }\limits_{{t_{0} - \tau \le s \le t_{0} }} W\left( s \right)} \right).$$
(37)

On the other hand, from (35) and (36), we have \(V\left( t \right) < hM_{0}\), \(- \tau \le t < t_{0}\) and \(V\left( {t_{0} } \right) = hM_{0}\). As a result, we get \(W\left( t \right) < hM_{0} e^{ - \lambda t}\), \(- \tau \le t < t_{0}\), it leads to \(\left( {\sup_{{t_{0} - \tau \le s \le t_{0} }} W\left( s \right)} \right) < e^{\lambda \tau } hM_{0} e^{{ - \lambda t_{0} }}\). This means \(e^{{\lambda t_{0} }} \left( {\sup_{{t_{0} - \tau \le s \le t_{0} }} W\left( s \right)} \right) < e^{\lambda \tau } hM_{0} = e^{\lambda \tau } V\left( {t_{0} } \right)\).

From (37), it can be deduced that \(\dot{S}\left( {t_{0} } \right) < \left( {\lambda - p_{1} + qe^{\lambda \tau } } \right)V\left( {t_{0} } \right) = 0\). This is a contradiction with inequality (35), thus, (34) is established. With (33), we get that

$$W\left( t \right) < hM_{0} e^{ - \lambda t} ,\quad \forall t \in \left[ { - \tau ,\theta T} \right]$$
(38)

Let \(\vartheta = p_{1} + p_{2}\), for \(t \in \left[ {\theta T,T} \right)\), we have \(Q\left( t \right) = V\left( t \right) - hM_{0} e^{{\vartheta \left( {t - \theta T} \right)}} < 0\). Otherwise, there exist a \(t_{1} \in \left[ {\theta T,T} \right)\), such that

$$Q\left( {t_{1} } \right) = 0,\quad \dot{Q}\left( {t_{1} } \right) \ge 0,$$
(39)
$$Q\left( t \right) < 0,\quad \theta T \le t < t_{1} .$$
(40)

For \(\tau > 0\), according to (38)–(40), we always have

$$\mathop {\sup }\limits_{{t_{1} - \tau \le s \le t_{1} }} W\left( s \right) < e^{\lambda \tau } W\left( {t_{1} } \right).$$

Then, one has \(\dot{Q}\left( {t_{1} } \right) < 0\), which is contradicts with (39). Thus, for \(t \in \left[ {\theta T,T} \right)\),

$$V\left( t \right) < hM_{0} e^{{\vartheta \left( {1 - \theta } \right)T}} .$$

With (33) and (34), we have

$$V\left( t \right) < hM_{0} e^{{\vartheta \left( {1 - \theta } \right)T}} ,\quad {\text{for}}\quad t \in \left[ { - \tau ,T} \right).$$

Similarly, we can prove

$$V\left( t \right) < hM_{0} e^{{\vartheta \left( {1 - \theta } \right)T}} ,\;\;{\text{for}}\;\;t \in \left[ {T,\left( {1 + \theta } \right)T} \right)\;\;{\text{and}}\;\;V\left( t \right) < hM_{0} e^{{\vartheta \left( {t - 2\theta } \right)T}} ,\;\;{\text{for}}\;\;t \in \left[ {\left( {1 + \theta } \right)T,2T} \right).$$

Through a mathematical induction, for any integer \(\omega = 0,1,2, \ldots\), we can derive the estimates of \(V\left( t \right)\) as

$$\left\{ {\begin{array}{ll} {V\left( t \right) < hM_{0} e^{{\omega \vartheta \left( {1 - \theta } \right)T}} \le hM_{0} e^{{\vartheta \left( {1 - \theta } \right)t}} ,} & {\omega T \le t < \left( {\omega + \theta } \right)T,} \\ {V\left( t \right) < hM_{0} e^{{\vartheta \left[ {t - \left( {m + 1} \right)\theta T} \right]}} \le hM_{0} e^{{\vartheta \left( {1 - \theta } \right)t}} ,} & {\left( {\omega + \theta } \right)T \le t < \left( {\omega + 1} \right)T.} \\ \end{array} } \right.$$

Let \(h \to 1\), from the definition of \(V\left( t \right)\), we have

$$W\left( t \right) \le M_{0} e^{{ - \left[ {\lambda - \vartheta \left( {1 - \theta } \right)} \right]t}} = \left( {\mathop {\sup }\limits_{ - \tau \le s \le 0} W\left( s \right)} \right)e^{ - \varpi t} ,\quad t \ge 0.$$

Therefore, for the error dynamical system (9) the zero solution is globally exponentially stable. The proof of Theorem 1 is thus completed.

Appendix 2

2.1 Proof of Theorem 3

Construct a piecewise Lyapunov candidate function

$$W\left( t \right) = \frac{1}{2}\sum\limits_{k = 1}^{m} {\sum\limits_{{i \in C_{k} }} {e_{ik}^{\text{T}} \left( t \right)e_{ik} \left( t \right)} } + \frac{1}{2}\varPi \left( t \right)\sum\limits_{k = 1}^{l} {ce^{{ - p_{1} t}} \frac{{\left( {d_{k} \left( t \right) - d_{k}^{*} } \right)^{2} }}{{h_{k} }}} ,$$

where \(d_{k}^{ * } > 0\), \(k = 1,2, \ldots ,l\) are constants, and \(\varPi \left( \cdot \right)\) is a piecewise function defined as

$$\varPi \left( s \right) = \left\{ {\begin{array}{ll} {1,} & { - \tau \le s \le 0,} \\ {e^{{p_{1} \omega T}} ,} & {\omega T \le s < \left( {\omega + 1} \right)T,\omega = 0,1,2, \ldots } \\ \end{array} } \right.$$

Obviously, while \(\omega = 0,1,2, \ldots\), we have

$$W\left( t \right) = \left\{ {\begin{array}{ll} {\frac{1}{2}\sum\limits_{k = 1}^{m} {\sum\limits_{{i \in C_{k} }} {e_{ik}^{\text{T}} \left( t \right)e_{ik} \left( t \right)} } + \frac{1}{2}e^{{ - p_{1} \left( {t - \omega T} \right)}} \sum\limits_{k = 1}^{l} {\frac{c}{{h_{k} }}\left( {d_{k} \left( t \right) - d_{k}^{*} } \right)^{2} } ,} & {\omega T \le t < \left( {\omega + \theta } \right)T,} \\ {\frac{1}{2}\sum\limits_{k = 1}^{m} {\sum\limits_{{i \in C_{k} }} {e_{ik}^{\text{T}} \left( t \right)e_{ik} \left( t \right)} } + \frac{1}{2}e^{{ - p_{1} \left( {t - \omega T} \right)}} \sum\limits_{k = 1}^{l} {\frac{c}{{h_{k} }}d_{k}^{{*^{2} }} } ,} & {\left( {\omega + \theta } \right)T \le t < \left( {\omega + 1} \right)T.} \\ \end{array} } \right.$$

With Assumptions 1 and 2, the time derivative of \(W\left( t \right)\) along the trajectory of (9) can be calculated as follows. When \(t \in \left[ {\omega T,\left( {\omega + \theta } \right)T} \right)\), \(\omega = 0,1,2, \ldots\), we have

$$\begin{aligned} \dot{W}\left( t \right) & \le E^{\rm T} \left( t \right)\left( {\left( {cZ - cD^{ * } + a_{1}^{0} I_{N} } \right) \otimes \varGamma } \right)E\left( t \right) + \frac{q}{2}\left( {\mathop {\sup }\limits_{t - \tau \le s \le t} \sum\nolimits_{k = 1}^{m} {E_{k}^{\rm T} \left( s \right)E_{k} \left( s \right)} } \right) \\ & \quad - a_{1}^{0} E^{\rm T} \left( t \right)\left( {I_{N} \otimes \varGamma } \right)E\left( t \right) - \frac{{p_{1} }}{2}e^{{ - p_{1} \left( {t - \omega T} \right)}} \sum\nolimits_{k = 1}^{l} {\frac{c}{{h_{k} }}\left( {d_{k} \left( t \right) - d_{k}^{ * } } \right)^{2} } , \\ \end{aligned}$$
(41)

where \(D^{ * }\) is a modified block diagonal matrix of \(D\) through replacing the principal-diagonal sub-matrices \(d_{k} I_{{r_{k} }}\) by \(\left( {e^{{ - p_{1} \theta T}} d_{k}^{ * } } \right)I_{{r_{k} }} ,k = 1,2, \ldots ,l\).

Set \(Q^{ * } = a_{1}^{0} I_{N} + cZ\), \(a_{1}^{0} I_{N} + cZ - cD^{ * } = Q^{ * } - cD^{ * } = \left( {\begin{array}{*{20}c} {G^{ * } - c\tilde{D}^{ * } } & 0 \\ {0^{\rm T} } & {Q_{r}^{ * } } \\ \end{array} } \right)\), where \(G^{ * } = \left( {\begin{array}{llll} {cZ_{11} + a_{1}^{0} I_{{r_{1} }} } & 0 & \cdots & 0 \\ 0 & {cZ_{22} + a_{1}^{0} I_{{r_{2} }} } & \cdots & 0 \\ \vdots & \vdots & \ddots & \vdots \\ 0 & 0 & \cdots & {cZ_{ll} + a_{1}^{0} I_{{r_{l} }} } \\ \end{array} } \right)\), \(\tilde{D}^{ * } = e^{{ - p_{1} \theta T}} \left( {\begin{array}{llll} {d_{1}^{ * } I_{{r_{1} }} } & 0 & \cdots & 0 \\ 0 & {d_{2}^{ * } I_{{r_{2} }} } & \cdots & 0 \\ \vdots & \vdots & \ddots & \vdots \\ 0 & 0 & \cdots & {d_{l}^{ * } I_{{r_{l} }} } \\ \end{array} } \right)\), and \(Q_{r}^{ * }\) is the minor matrix of \(Q^{ * }\) by removing its first \(\left( {r_{1} + r_{2} + \cdots + r_{l} } \right)\) row–column pairs. It is obviously that \(Q^{ * }\) is a real symmetric matrix. From the pinning condition \(\left( {{\text{i}}^{\prime \prime } } \right)\), we conclude that

$$\begin{aligned} \lambda_{\hbox{max} } \left( {cZ_{kk} + a_{1}^{0} I_{{r_{k} }} } \right) & = \lambda_{\hbox{max} } \left( {c\varTheta_{kk} + L_{k}^{0} I_{{r_{k} }} + a_{1}^{0} I_{{r_{k} }} } \right) \\ & < c\lambda_{\hbox{max} } \left( {\varTheta_{kk} } \right) + L_{k}^{0} + a_{1}^{0} < 0,\quad l + 1 \le k \le m. \\ \end{aligned}$$

This means \(Q_{r}^{ * } < 0\). As a result, for \(k = 1,2, \cdots ,l\), when \(d_{k}^{ * } > 0\) are large enough such that \(d_{k}^{ * } > \frac{{e^{{p_{1} \theta T}} \lambda_{\hbox{max} } \left( {G^{ * } } \right)}}{c}\). It is easy to get \(Q^{ * } - cD^{ * } < 0\). This follows Lemma 2 directly.

With (41), we can deduce that

$$\begin{aligned} \dot{W}\left( t \right) & \le - \frac{{p_{1} }}{2}\sum\limits_{k = 1}^{m} {\sum\limits_{{i \in C_{k} }} {e_{ik}^{\text{T}} \left( t \right)e_{ik} \left( t \right)} } - \frac{{p_{1} }}{2}e^{{ - p_{1} \left( {t - \omega T} \right)}} \sum\limits_{k = 1}^{l} {\frac{c}{{h_{k} }}\left( {d_{k} \left( t \right) - d_{k}^{*} } \right)^{2} } \\ & \quad + \frac{q}{2}\left( {\mathop {\sup }\limits_{t - \tau \le s \le t} \sum\limits_{k = 1}^{m} {\sum\limits_{{i \in C_{k} }} {e_{ik}^{\text{T}} \left( s \right)e_{ik} \left( s \right)} } } \right) \\ & \le - p_{1} W\left( t \right) + q\left( {\mathop {\sup }\limits_{t - \tau \le s \le t} W\left( s \right)} \right). \\ \end{aligned}$$

In the same way, when \(t \in \left[ {\left( {\omega + \theta } \right)T,\left( {\omega + 1} \right)T} \right)\), \(\omega = 0,1,2, \ldots\), we obtain that

$$\dot{W}\left( t \right) \le p_{2} W\left( t \right) + q\left( {\mathop {\sup }\limits_{t - \tau \le s \le t} W\left( s \right)} \right).$$

Namely,

$$\left\{ {\begin{array}{ll} {\dot{W}\left( t \right) \le - p_{1} W\left( t \right) + q\left( {\mathop {\sup }\limits_{t - \tau \le s \le t} W\left( s \right)} \right),} & {\omega T \le t < \left( {\omega + \theta } \right)T,} \\ {\dot{W}\left( t \right) \le p_{2} W\left( t \right) + q\left( {\mathop {\sup }\limits_{t - \tau \le s \le t} W\left( s \right)} \right),} & {\left( {\omega + \theta } \right)T \le t < \left( {\omega + 1} \right)T.} \\ \end{array} } \right.$$

Then, by a similar proof of Theorem 1, we can prove that condition (15) means

$$\dot{W}\left( t \right) \le \left( {\mathop {\sup }\limits_{ - \tau \le s \le 0} W\left( s \right)} \right)e^{ - \varpi t} ,\quad t \ge 0.$$

Therefore, the global cluster lag synchronization of the delayed dynamical network (6) under the adaptive intermittent pinned controllers (16)–(17) is realizable. The proof of Theorem 3 is thus completed.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, F., Li, H., Chen, G. et al. Cluster lag synchronization of delayed heterogeneous complex dynamical networks via intermittent pinning control. Neural Comput & Applic 31, 7945–7961 (2019). https://doi.org/10.1007/s00521-018-3618-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-018-3618-7

Keywords