Abstract
Our paper devotes to synchronization issue of random coupling delayed complex networks with adaptive coupling strength as well as random coupling strength via intermittent control. Different from related literature, intermittent control in this paper is aperiodic. Furthermore, we assume coupling delay is a random variable and coupling function is nonlinear. Meanwhile, adaptive and random coupling strength are, respectively, taken into account in our model compared with most existing literature. Some novel sufficient conditions are derived by utilizing Lyapunov method and graph theory. What is more, synchronization of a second-order Kuramoto model, as an application of our theoretical results, is investigated. Meanwhile, some sufficient conditions are given as well. Eventually, some numerical simulations are given to show the effectiveness of results.
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Acknowledgements
The authors really appreciate the valuable comments of the editors and reviewers. This work was supported by Shandong Province Natural Science Foundation (Nos. ZR2018MA005, ZR2018MA020, ZR2017MA008); the Key Project of Science and Technology of Weihai (No. 2014DXGJMS08) and the Innovation Technology Funding Project in Harbin Institute of Technology (No. HIT.NSRIF.201703).
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Appendix
Appendix
1.1 Proof of Theorem 1
Proof
To start with, let \(V(e,t)=\sum _{k=1}^{N}c_{k}V_{k}(e_{k},t)\), where \( V_{k}(e_{k},t)=e_{k}^{\mathrm {T}}e_{k}+e^{-\rho t}\left( a(t)-a_{0}\right) ^{2}\). Considering digraph \(({\mathcal {G}},B)\) is strongly connected, in view of Proposition 2.1 in [49], we can easily avail that \(c_{k}>0\). And we have
where the differential operator \({\mathcal {L}}\) can be discovered in [25]. Based on Assumption 2, the following inequalities can be yielded that
and
The following inequality can be obtained by analogical derivation of (21)
In view of (10), one can acquire that
According to (4), when \(t\in [t_{m},s_{m})\), one has \(e_{k}^{\mathrm {T}}(t)u_{k}(t)=-d_{k}e_{k}^{\mathrm {T}}(t)e_{k}(t)\). In what follows, we can obtain by applying (20)–(23)
According to Theorem 2.2 in [49], we can get
where \(C_{{\mathcal {Q}}}\) denotes the directed cycle of \({\mathcal {Q}}\). We can get that
Therefore, one has
It is easy to get that
By a similar method, we can obtain
Therefore, one get
For any \(t\in [s_{m},t_{m+1})\), we can similarly compute the \({\mathcal {L}}V_{k}(e_{k}(t),t)\) as follows:
Thus, one get
Then, combining (25) with (26), we can conclude that
Therefore, one get
On account of (11) and Lemma 1, we can obtain that
What is more, by virtue of the definition of V(e(t), t), it yields
Consequently, according to (27) and (28), we can deduce
Hence, in view of Definition 1, systems (1) and (2) with adaptive coupling strength achieve exponential synchronization in mean square. Then, the proof is completed. \(\square \)
1.2 Proof of Theorem 2
Proof
According to Assumption 2 and (8), error system (5) can be converted to
Next, let \(V(e,t)=\sum _{h=1}^{N}c_{k}V_{k}(e_{k},t)\), where \(V_{k}(e_{k},t)=|e_{k}|^{p}\), \(c_{k}\) stands for the cofactor of the kth diagonal element of Laplacian matrix of digraph \(({\mathcal {G}}, B)\), where \(B=(a_{kh}b_{kh})_{N\times N}\). What is more, by virtue of Proposition 2.1 in [49], we can discover that \(c_{k}>0\).
By using the Young’s inequality,
and Assumption 2, it is easy to see that
Furthermore, similar to (29), it yields
Therefore, when \(t\in [t_{m},s_{m})\), the following inequality can be deduced
In virtue of Theorem 2.2 in [49], we can see that
Besides, one can deduce as follows:
Hence, it yields by (30)
Similarly, when \(t\in [s_{m},t_{m+1})\), one has
Consequently, combining (30) with (31), we have
In view of (12) and Lemma 1, we can obtain that
Moreover, we obtain
Thus, it is clear that
which indicates systems (1) and (2) with random coupling strength reach pth moment exponential synchronization by virtue of Definition 1. Then, the proof is completed. \(\square \)
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Wu, Y., Li, Y. & Li, W. Synchronization of random coupling delayed complex networks with random and adaptive coupling strength. Nonlinear Dyn 96, 2393–2412 (2019). https://doi.org/10.1007/s11071-019-04930-w
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DOI: https://doi.org/10.1007/s11071-019-04930-w