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A Novel Fast Fixed-Time Control Strategy and Its Application to Fixed-Time Synchronization Control of Delayed Neural Networks

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Abstract

In this paper, we emphasize on a novel fast fixed-time control strategy and its application to fixed-time synchronization control of semi-Markov jump delayed Cohen-Grossberg neural networks (SMJDCGNNs). First, we consider a class of SMJDCGNNs. Second, a novel fast fixed-time control strategy is proposed and designed to control the considered delayed system to achieve global synchronization within the derived fixed settling time. Third, the advantages of the derived theoretical results are discussed. Finally, we give a numerical example to show the effectiveness and feasibility of the obtained theoretical results.

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References

  1. Ali M, Saravanan S, Palanisamy L (2019) Stochastic finite-time stability of reaction-diffusion Cohen-Grossberg neural networks with time-varying delays. Chin J Phys 57:314–328

    Google Scholar 

  2. Su S, Lin Z (2020) An exploration of the Razumikhin stability theorem with applications in stabilization of delay systems. Automatica 119(109082):1–8

    MathSciNet  MATH  Google Scholar 

  3. Li Z, Yan H, Zhang H, Peng Y, Park J, He Y (2020) Stability analysis of linear systems with time-varying delay via intermediate polynomial-based functions. Automatica 113(108756):1–6

    MathSciNet  MATH  Google Scholar 

  4. Luo M, Cheng J, Liu X, Zhong S (2019) An extended synchronization analysis for memristor-based coupled neural networks via aperiodically intermittent control. Appl Math Comput 344–345:163–182

    MathSciNet  MATH  Google Scholar 

  5. Li L, Liu X, Tang M, Zhang S, Zhang X (2021) Asymptotical synchronization analysis of fractional-order complex neural networks with non-delayed and delayed couplings. Neurocomputing 445:180–193

    Google Scholar 

  6. Li X, Yang X, Cao J (2020) Event-triggered impulsive control for nonlinear delay system. Automatica 117(108981):1–7

    MathSciNet  Google Scholar 

  7. Liu X, Wu K, Zhang W (2020) Intermittent boundary stabilization of stochastic reaction-diffusion Cohen-Grossberg neural networks. Neural Netw 131:1–13

    MATH  Google Scholar 

  8. Wang W (2007) Approach of adaptive synchronization for bistatic SAR real-time imaging. IEEE Geosci Remote Sens 45(9):2695–2700

    Google Scholar 

  9. Guo Q, Wang Q, Li X (2019) Finite-time convergent control of electrohydraulic velocity servo system under uncertain parameter and external load. IEEE Trans Ind Electron 66(6):4513–4523

    Google Scholar 

  10. Hu J, Lanzon A (2019) Distributed finite-time consensus control for heterogeneous battery energy storage systems in droop-controlled microgrids. IEEE Trans Smart Grid 10(5):4751–4761

    Google Scholar 

  11. Ran G, Liu J, Li C, Chen L, Li D (2021) Event-based finite-time consensus control of second-order delayed multi-agent systems. IEEE Trans Circuits Syst II Exp Briefs 68(1):276–280

    Google Scholar 

  12. Zhang Z, Cao J (2019) Novel finite-time synchronization criteria for inertial neural networks with time delays via integral inequality method. IEEE Trans Neural Netw Learn Syst 30(5):1476–1485

    MathSciNet  Google Scholar 

  13. Liu J, Ran G, Huang Y, Han C, Yu Y, Sun C (2021) Adaptive event-triggered finite-time dissipative filtering for interval type-2 fuzzy markov jump systems with asynchronous modes. IEEE Trans Cybern. https://doi.org/10.1109/TCYB.2021.3053627

    Article  Google Scholar 

  14. Polyakov A (2012) Nonlinear feedback design for fixed-time control stabilization of linear control systems. IEEE Trans Autom Control 57(8):2106–2110

    MathSciNet  MATH  Google Scholar 

  15. Liu J, Zhang Y, Sun C, Yu Y (2019) Fixed-time consensus of multi-agent systems with input delay and uncertain disturbances via event-triggered control. Inf Sci 480:261–272

    MathSciNet  MATH  Google Scholar 

  16. Liu J, Zhang Y, Yu Y, Sun C (2020) Fixed-time leader-follower consensus of networked nonlinear systems via event/self-triggered control. IEEE Trans Neural Netw Learn Syst 31(11):5029–5037

    MathSciNet  Google Scholar 

  17. Su B, Wang H, Wang Y, Gao J (2021) Fixed-time formation of AUVs with disturbance via event-triggered control. Int J Control Autom 19(4):1505–1518

    MATH  Google Scholar 

  18. Chen C, Li L, Peng H, Kurths J, Yang Y (2018) Fixed-time synchronization of hybrid coupled networks with time-varying delays. Chaos Solitons Fract 108:49–56

    MathSciNet  MATH  Google Scholar 

  19. Wang X, Cao J, Wang J, Qi J (2021) A novel fixed-time stability strategy and its application to fixed-time synchronization control of semi-Markov jump delayed neural networks. Neurocomputing 452:284–293

    Google Scholar 

  20. Wang X, Fang J, Zhou W (2020) Fixed-time synchronization control for a class of nonlinear coupled Cohen-Grossberg neural networks from synchronization dynamics viewpoint. Neurocomputing 400:371–380

    Google Scholar 

  21. Wang X, Fang J, Zhou W (2019) Controller design for fixed-time synchronization of nonlinear coupled Cohen-Grossberg neural networks with switching parameters and time-varying delays based on synchronization dynamics analysis. Nonlinear Dyn 98(3):2079–2096

    MATH  Google Scholar 

  22. Aouiti C, Dridi F (2019) New results on impulsive Cohen-Grossberg neural networks. Neural Process Lett 49:1459–1483

    MATH  Google Scholar 

  23. Zhang L, Yang Y, Xu X (2018) Synchronization analysis for fractional order memristive Cohen-Grossberg neural networks with state feedback and impulsive control. Physica A 506:644–660

    MathSciNet  Google Scholar 

  24. Liu M, Jiang H, Hu C (2019) New results for exponential synchronization of memristive Cohen-Grossberg neural networks with time-varying delays. Neural Process Lett 49:79–102

    Google Scholar 

  25. Qiu S, Huang Y, Ren S (2018) Finite-time synchronization of coupled Cohen-Grossberg neural networks with and without coupling delays. J Frankl Inst 355:4379–4403

    MathSciNet  MATH  Google Scholar 

  26. Shi Y, Cao J (2020) Finite-time synchronization of memristive Cohen-Grossberg neural networks with time delays. Neurocomputing 377:159–167

    Google Scholar 

  27. Jia S, Hu C, Yu J, Jiang H (2018) Asymptotical and adaptive synchronization of Cohen-Grossberg neural networks with heterogeneous proportional delays. Neurocomputing 275:1449–1455

    Google Scholar 

  28. Chen W, Huang Y, Ren S (2018) Passivity and robust passivity of delayed Cohen-Grossberg neural networks with and without reaction-diffusion terms. Circ Syst Signal Pr 37(7):2772–2804

    MathSciNet  MATH  Google Scholar 

  29. Xiao J, Zeng Z, Wu A, Wen S (2020) Fixed-time synchronization of delayed Cohen-Grossberg neural networks based on a novel sliding mode. Neural Netw 128:1–12

    MATH  Google Scholar 

  30. Chen W, Huang Y, Ren S (2018) Passivity and synchronization of coupled reaction-diffusion Cohen-Grossberg neural networks with state coupling and spatial diffusion coupling. Neurocomputing 275:1208–1218

    Google Scholar 

  31. Ren F, Jiang M, Xu H, Li M (2020) Quasi fixed-time synchronization of memristive Cohen-Grossberg neural networks with reaction-diffusion. Neurocomputing 415:74–83

    Google Scholar 

  32. Cohen M, Grossberg S (1983) Absolute stability of global pattern formation and parallel memory storage by competitive neural networks. IEEE Trans Syst Man Cybern 13(5):815–826

    MathSciNet  MATH  Google Scholar 

  33. Li B, Song Q (2016) Some new results on periodic solution of Cohen-Grossberg neural network with impulses. Neurocomputing 177:401–408

    Google Scholar 

  34. Zhou Y, Li C, Chen L, Huang T (2018) Global exponential stability of memristive Cohen-Grossberg neural networks with mixed delays and impulse time window. Neurocomputing 275:2384–2391

    Google Scholar 

  35. Wang D, Huang L, Tang L, Zhuang J (2018) Generalized pinning synchronization of delayed Cohen-Grossberg neural networks with discontinuous activations. Neural Netw 104:80–92

    MATH  Google Scholar 

  36. Li R, Cao J, Alsaedi A, Alsaadi F (2017) Exponential and fixed-time synchronization of Cohen-Grossberg neural networks with time-varying delays and reaction-diffusion terms. Appl Math Comput 313:37–51

    MathSciNet  MATH  Google Scholar 

  37. Huang Y, Qiu S, Ren S, Zheng Z (2018) Fixed-time synchronization of coupled Cohen-Grossberg neural networks with and without parameter uncertainties. Neurocomputing 315:157–168

    Google Scholar 

  38. Mao X, Yuan C (2006) Stochastic differential equations with Markovian switching. Imperial College Press, London

    MATH  Google Scholar 

  39. Liu Y, Zhao S, Chu D, Li W (2020) Synchronized stationary distribution and synchronization of stochastic coupled networks with Markovian switching via periodically intermittent control. Neurocomputing 410:28–40

    Google Scholar 

  40. Ran G, Li C, Lam H, Li D, Han C (2020) Event-based dissipative control of interval type-2 fuzzy Markov jump systems under sensor saturation and actuator nonlinearity. IEEE Trans Fuzzy Syst. https://doi.org/10.1109/TFUZZ.2020.3046335

    Article  Google Scholar 

  41. Yang J, Bu X, Yu Q, Zhu F (2020) Observer-based controller design for nonlinear semi-Markov switched system with external disturbance. J Frankl Inst 357:8435–8453

    MathSciNet  MATH  Google Scholar 

  42. Wang B, Zhu Q (2020) Stability analysis of discrete-time semi-Markov jump linear systems with partly unknown semi-Markov kernel. Syst Control Lett 140(104688):1–8

    MathSciNet  MATH  Google Scholar 

  43. Liu J, Wu H, Cao J (2020) Event-triggered synchronization in fixed time for semi-Markov switching dynamical complex networks with multiple weights and discontinuous nonlinearity. Commun Nonlinear Sci Numer Simul 90(105400):1–21

    MathSciNet  MATH  Google Scholar 

  44. Wang B, Zhu Q (2020) The novel sufficient conditions of almost sure exponential stability for semi-Markov jump linear systems. Syst Control Lett 137(104622):1–8

    MathSciNet  MATH  Google Scholar 

  45. Wang H, Xue B, Xue A (2021) Leader-following consensus control for semi-Markov jump multi-agent systems: an adaptive event-triggered scheme. J Frankl Inst 358(1):428–447

    MathSciNet  MATH  Google Scholar 

  46. Atencia M, Joya G, Sandoval F (2013) Identification of noisy dynamical systems with parameter estimation based on Hopfield neural networks. Neurocomputing 121:14–24

    Google Scholar 

  47. Yan Y, Zhao K, Cao J, Ma H (2021) Prediction research of cervical cancer clinical events based on recurrent neural networks. Procedia Comput Sci 183:221–229

    Google Scholar 

  48. Zuo Z, Tie L (2016) Distributed robust finite-time nonlinear consensus protocols for multi-agent systems. Int J Syst Sci 47(6):1366–1375

    MathSciNet  MATH  Google Scholar 

  49. Huang Q, Cao J (2018) Stability analysis of inertial Cohen-Grossberg neural networks with Markovian jumping parameters. Neurocomputing 282:89–97

    Google Scholar 

  50. Huang Y, Chen W, Wang J (2018) Finite-time passivity of delayed multi-weighted complex dynamical networks with different dimensional nodes. Neurocomputing 312:74–89

    Google Scholar 

  51. Hu C, Yu J, Jiang H (2014) Finite-time synchronization of delayed neural networks with Cohen-Grossberg type based on delayed feedback control. Neurocomputing 143:90–96

    Google Scholar 

  52. Zhao Y, Tao J, Shi N (2010) A note on observer design for one-sided Lipschitz nonlinear systems. Syst Control Lett 59:66–71

    MathSciNet  MATH  Google Scholar 

  53. Xu Y, Meng D, Xie C, You G, Zhou W (2018) A class of fast fixed-time synchronization control for the delayed neural network. J Frankl Inst 355:164–176

    MathSciNet  MATH  Google Scholar 

  54. Guo J, Fu Z, Zhang S (2019) Adaptive fixed-time attitude tracking control for rigid spacecraft with actuator faults. IEEE Trans Ind Electron 66(9):7141–7149

    Google Scholar 

  55. Ni J, Liu L, Liu C, Hu X, Li S (2017) Fast fixed-time nonsingular terminal sliding mode control and its application to chaos suppression in power system. IEEE Trans Circuits Syst II-Express Briefs 64(2):151–155

    Google Scholar 

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Acknowledgements

The work is supported by Key Project of the National Natural Science Foundation of China under Grant No. 61833005, the National Natural Science Foundation of China under Grant No. 62006093 and the Natural Science Foundation for colleges and universities in Jiangsu Province of China under Grant No. 19KJB520024.

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Authors and Affiliations

  1. School of Computer Science and Technology, Huaiyin Normal University, Huaian, Jiangsu, 223300, China

    Xin Wang, Jiangtao Wang, Jinshan Qi & Qingying Sun

  2. School of Mathematics, Southeast University, Nanjing, 21099, China

    Jinde Cao

  3. Yonsei Frontier Lab, Yonsei University, Seoul, 03722, South Korea

    Jinde Cao

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  1. Xin Wang
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  5. Qingying Sun
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Correspondence to Xin Wang.

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Wang, X., Cao, J., Wang, J. et al. A Novel Fast Fixed-Time Control Strategy and Its Application to Fixed-Time Synchronization Control of Delayed Neural Networks. Neural Process Lett 54, 145–164 (2022). https://doi.org/10.1007/s11063-021-10624-5

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