Skip to main content
Log in

Projective synchronization for two nonidentical time-delayed fractional-order T–S fuzzy neural networks based on mixed \({H_\infty }\)/passive adaptive sliding mode control

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

This paper deals with the problem of mixed \({H_\infty }\)/passive projective synchronization for two different fractional-order (FO) T–S fuzzy neural networks with uncertain parameters and time delays. Firstly, a fractional integral sliding surface which is suitable for the considered FO error system is proposed. Second, in terms of the established sliding surface, combining a novel reaching law, a new adaptive sliding mode control law is introduced, which can force the closed-loop dynamic error system trajectories to reach the sliding surface. Then, by giving a continuous frequency distributed model of the FO dynamic networks and the application of FO system stability theory, the projective synchronization conditions are addressed in terms of linear matrix inequality techniques. Based on the conditions, a desired controller which can guarantee the robust stability of the closed-loop system and also ensure a mixed \({H_\infty }\)/passive performance level is designed. Finally, synchronization of two nonidentical time-delayed FO T–S fuzzy neural networks with uncertain parameters as a simulation example is given to illustrate the effectiveness of the proposed method.

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

Access this article

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

Similar content being viewed by others

References

  1. Podlubny I (1999) Fractional-order systems and -controllers. IEEE Trans Autom Control 44(1):208–214

    Article  MathSciNet  MATH  Google Scholar 

  2. Feng J, Ma Q, Qin S (2017) Exponential stability of periodic solution for impulsive memristor-based Cohen-Grossberg neural networks with mixed delays. Int J Pattern Recognit Artif Intell 31(7). https://doi.org/10.1142/S0218001417500227

  3. Chen H, Wu L, Dou Q et al (2017) Ultrasound standard plane detection using a composite neural network framework. IEEE Trans Cybern 47(6):1576–1586

    Article  Google Scholar 

  4. Tong C, Li J, Zhu F (2017) A convolutional neural network based method for event classification in event-driven multi-sensor network. Comput Electr Eng 60:90–99

    Article  Google Scholar 

  5. Yang X, Feng Z, Feng J et al (2017) Synchronization of discrete-time neural networks with delays and Markov jump topologies based on tracker information. Neural Netw 85:157–164

    Article  Google Scholar 

  6. Xu S, Lam J (2006) A new approach to exponential stability analysis of neural networks with time-varying delays. Neural Netw 19(1):76–83

    Article  MATH  Google Scholar 

  7. Wang Z, Liu Y, Li M et al (2006) Stability analysis for stochastic Cohen–Grossberg neural networks with mixed time delays. IEEE Trans Neural Netw 17(3):814–820

    Article  Google Scholar 

  8. Rakkiyappan R, Cao J, Velmurugan G (2015) Existence and uniform stability analysis of fractional-order complex-valued neural networks with time delays. IEEE Trans Neural Netw Learn Syst 26(1):84–97

    Article  MathSciNet  MATH  Google Scholar 

  9. Zhang B, Lam J, Xu S (2015) Stability analysis of distributed delay neural networks based on relaxed Lyapunov–Krasovskii functionals. IEEE Trans Neural Netw Learn Syst 26(7):1480–1492

    Article  MathSciNet  Google Scholar 

  10. Wang H, Yu Y, Wen G (2014) Stability analysis of fractional-order Hopfield neural networks with time delays. Neural Netw 55:98–109

    Article  MATH  Google Scholar 

  11. Wang F, Yang Y, Hu M (2015) Asymptotic stability of delayed fractional-order neural networks with impulsive effects. Neurocomputing 154:239–244

    Article  Google Scholar 

  12. Wu R, Hei X, Chen L (2013) Finite-time stability of fractional-order neural networks with delay. Commun Theor Phys 60(2):189–193

    Article  MathSciNet  MATH  Google Scholar 

  13. Yang X, Song Q, Liu Y et al (2015) Finite-time stability analysis of fractional-order neural networks with delay. Neurocomputing 152:19–26

    Article  Google Scholar 

  14. Yang X, Li C, Song Q et al (2016) Mittag–Leffler stability analysis on variable-time impulsive fractional-order neural networks. Neurocomputing 207:276–286

    Article  Google Scholar 

  15. Chen J, Zeng Z, Jiang P (2014) Global Mittag–Leffler stability and synchronization of memristor-based fractional-order neural networks. Neural Netw 51:1–8

    Article  MATH  Google Scholar 

  16. Mainieri R, Rehacek J (1999) Projective synchronization in three-dimensioned chaotic systems. Phys Rev Lett 82:3042–3045

    Article  Google Scholar 

  17. Jia Q (2007) Projective synchronization of a new hyperchaotic Lorenz system. Phys Lett A 370:40–45

    Article  MATH  Google Scholar 

  18. Wang ZL (2010) Projective synchronization of hyperchaotic Lü system and Liu system. Nonlinear Dyn 59(3):455–462

    Article  MathSciNet  MATH  Google Scholar 

  19. Agrawal SK, Srivastava M, Das S (2012) Synchronization of fractional order chaotic systems using active control method. Chaos Solitons Fractals 45(6):737–752

    Article  Google Scholar 

  20. Wang X, Zhang X, Ma C (2012) Modified projective synchronization of fractional-order chaotic systems via active sliding mode control. Nonlinear Dyn 69(1–2):511–517

    Article  MathSciNet  MATH  Google Scholar 

  21. Andrew LYT, Li XF, Chu YD et al (2015) A novel adaptive-impulsive synchronization of fractional-order chaotic systems. Chin Phys B 24(10):86–92

    Article  Google Scholar 

  22. Ding L (2009) Projective synchronization of fractional-order chaotic systems based on sliding mode control. Acta Phys Sin 58(6):3747–3752

    MathSciNet  MATH  Google Scholar 

  23. Bai J, Yu Y, Wang S et al (2012) Modified projective synchronization of uncertain fractional order hyperchaotic systems. Commun Nonlinear Sci Numer Simul 17(4):1921–1928

    Article  MathSciNet  MATH  Google Scholar 

  24. Zhou P, Zhu W (2011) Function projective synchronization for fractional-order chaotic systems. Nonlinear Anal Real World Appl 12(2):811–816

    Article  MathSciNet  MATH  Google Scholar 

  25. Yang YH, Xiao J, Ma ZZ (2013) Modified function projective synchronization for a class of partially linear fractional order chaotic systems. Acta Phys Sin 62(18):116–121

    Google Scholar 

  26. Bao H, Cao J (2015) Projective synchronization of fractional-order memristor-based neural networks. Neural Netw 63:1–9

    Article  MATH  Google Scholar 

  27. Yu J, Hu C, Jiang H et al (2014) Projective synchronization for fractional neural networks. Neural Netw 49:87–95

    Article  MATH  Google Scholar 

  28. Velmurugan G, Rakkiyappan R (2015) Hybrid projective synchronization of fractional-order memristor-based neural networks with time delays. Nonlinear Dyn 11(3):1–14

    MATH  Google Scholar 

  29. Song X, Liu L, Balsera IT et al (2016) Output feedback control for fractional-order Takagi–Sugeno fuzzy systems with unmeasurable premise variables. Trans Inst Meas Control 38(10):1201–1211

    Article  Google Scholar 

  30. Song X, Xu S, Shen H (2008) Robust control for uncertain fuzzy systems with distributed delays via output feedback controllers. Inf Sci 178(22):4341–4356

    Article  MathSciNet  MATH  Google Scholar 

  31. Li Y, Li J (2015) Decentralized stabilization of fractional order T–S fuzzy interconnected systems with multiple time delays. J Intell Fuzzy Syst 30:319–331

    Article  MATH  Google Scholar 

  32. Yucel E, Ali MS, Gunasekaran N et al (2016) Sampled-data filtering of Takagi–Sugeno fuzzy neural networks with interval time-varying delays. Fuzzy Sets Syst 316:69–81

    Article  MathSciNet  MATH  Google Scholar 

  33. Shi P, Zhang Y, Chadli M et al (2016) Mixed H-infinity and passive filtering for discrete fuzzy neural networks with stochastic jumps and time delays. IEEE Trans Neural Netw Learn Syst 27(4):903–909

    Article  MathSciNet  Google Scholar 

  34. Choi HD, Ahn CK, Shi P et al (2015) Filtering for Takagi-Sugeno fuzzy neural networks based on Wirtinger-type inequalities. Neurocomputing 153:117–125

    Article  Google Scholar 

  35. Song S, Song X, Balsera IT (2017) Adaptive projective synchronization for fractional-order T–S fuzzy neural networks with time-delay and uncertain parameters. Optik 129:140–152

    Article  Google Scholar 

  36. Wu L, Su X, Shi P (2012) Sliding mode control with bounded ℒ2 gain performance of Markovian jump singular time-delay systems. Automatica 48(8):1929–1933

    Article  MathSciNet  MATH  Google Scholar 

  37. Niu Y, Wang X (2009) Sliding mode control design for uncertain delay systems with partial actuator degradation. Int J Syst Sci 40(4):403–409

    Article  MathSciNet  MATH  Google Scholar 

  38. Lin TC, Lee TY (2011) Chaos synchronization of uncertain fractional-order chaotic systems with time delay based on adaptive fuzzy sliding mode control. IEEE Trans Fuzzy Syst 19(4):623–635

    Article  Google Scholar 

  39. Lin TC, Lee TY, Balas VE (2011) Adaptive fuzzy sliding mode control for synchronization of uncertain fractional order chaotic systems. Chaos Solitons Fractals 44(10):791–801

    Article  MATH  Google Scholar 

  40. Shen H, Xu S, Lu J et al (2012) Passivity-based control for uncertain stochastic jumping systems with mode-dependent round-trip time delays. J Franklin Inst 349(5):1665–1680

    Article  MathSciNet  MATH  Google Scholar 

  41. Gao H, Chen T, Chai T (2007) Passivity and passification for networked control systems. SIAM J Control Optim 46(4):1299–1322

    Article  MathSciNet  MATH  Google Scholar 

  42. Kuntanapreeda S (2016) Adaptive control of fractional-order unified chaotic systems using a passivity-based control approach. Nonlinear Dyn 84(4):2505–2515

    Article  MathSciNet  MATH  Google Scholar 

  43. Xu S, Chen T, Lam J (2003) Robust filtering for uncertain Markovian jump systems with mode-dependent time delays. IEEE Trans Autom Control 48(5):900–907

    Article  MathSciNet  MATH  Google Scholar 

  44. Shen J, Lam J (2014) State feedback control of commensurate fractional-order systems. Int J Syst Sci 45(3):363–372

    Article  MathSciNet  MATH  Google Scholar 

  45. Shen J, Lam J (2014) model reduction for positive fractional order systems. Asian J Control 16(2):441–450

    Article  MathSciNet  MATH  Google Scholar 

  46. Mathiyalagan K, Park JH, Sakthivel R et al (2014) Robust mixed and passive filtering for networked Markov jump systems with impulses. Sig Process 101:162–173

    Article  Google Scholar 

  47. Shen H, Wu ZG, Park JH (2015) Reliable mixed passive and filtering for semi-Markov jump systems with randomly occurring uncertainties and sensor failures. Int J Robust Nonlinear Control 25(17):3231–3251

    Article  MathSciNet  MATH  Google Scholar 

  48. Su L, Shen H (2015) Mixed /passive synchronization for complex dynamical networks with sampled-data control. Appl Math Comput 259:931–942

    MathSciNet  MATH  Google Scholar 

  49. Podlubny I (1999) Fractional differential equations. Academic Press, San Diego

    MATH  Google Scholar 

  50. Xie L, de Souza Carlos E (1992) Robust control for linear systems with norm-bounded time-varying uncertainty. IEEE Trans Autom Control 37(8):1188–1191

    Article  MathSciNet  MATH  Google Scholar 

  51. Gai M, Cui S, Liang S et al (2016) Frequency distributed model of Caputo derivatives and robust stability of a class of multi-variable fractional-order neural networks with uncertainties. Neurocomputing 202:91–97

    Article  Google Scholar 

  52. Ding Z, Shen Y (2016) Projective synchronization of nonidentical fractional-order neural networks based on sliding mode controller. Neural Netw 76:97–105

    Article  Google Scholar 

  53. Wu H, Wang L, Wang Y et al (2016) Global Mittag-Leffler projective synchronization for fractional-order neural networks: an LMI-based approach. Adv Differ Equ 132:1–18

    MathSciNet  MATH  Google Scholar 

  54. Mazandarani M, Kamyad AV (2013) Modified fractional Euler method for solving Fuzzy fractional initial value problem. Commun Nonlinear Sci Numer Simul 18(1):12–21

    Article  MathSciNet  MATH  Google Scholar 

  55. Mazandarani M, Najariyan M (2014) Type-2 fuzzy fractional derivatives. Commun Nonlinear Sci Numer Simul 19(7):2354–2372

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

Project supported by National Natural Science Foundation of China (Nos. U1604146, 61203047), Science and Technology Research Project in Henan Province (Nos. 152102210273, 162102410024), Foundation for the University Technological Innovative Talents of Henan Province (No. 18HASTIT019).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Xiaona Song.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Song, S., Song, X. & Tejado, I. Projective synchronization for two nonidentical time-delayed fractional-order T–S fuzzy neural networks based on mixed \({H_\infty }\)/passive adaptive sliding mode control. Int. J. Mach. Learn. & Cyber. 10, 799–812 (2019). https://doi.org/10.1007/s13042-017-0761-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-017-0761-x

Keywords

Navigation