Robust control of chaos in modified FitzHugh-Nagumo neuron model under external electrical stimulation based on internal model principle

Yuan Jiang; Jiyang Dai

Kybernetika (2011)

  • Volume: 47, Issue: 4, page 612-629
  • ISSN: 0023-5954

Abstract

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This paper treats the question of robust control of chaos in modified FitzHugh-Nagumo neuron model under external electrical stimulation based on internal model principle. We first present the solution of the global robust output regulation problem for output feedback system with nonlinear exosystem. Then we show that the robust control problem for the modified FitzHugh-Nagumo neuron model can be formulated as the global robust output regulation problem and the solvability conditions for the output regulation problem for the modified FitzHugh-Nagumo neuron model are all satisfied. Then we apply the obtained output regulation results to the control problem for modified FitzHugh-Nagumo neuron model. Finally, an output feedback control law is designed for the modified FitzHugh-Nagumo neuron model to achieve global stability of the closed-loop system in the presence of uncertain parameters and external stimulus. An example is shown that the proposed algorithm can completely reject the external electrical stimulation generated from a Van der Pol circuit.

How to cite

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Jiang, Yuan, and Dai, Jiyang. "Robust control of chaos in modified FitzHugh-Nagumo neuron model under external electrical stimulation based on internal model principle." Kybernetika 47.4 (2011): 612-629. <http://eudml.org/doc/196901>.

@article{Jiang2011,
abstract = {This paper treats the question of robust control of chaos in modified FitzHugh-Nagumo neuron model under external electrical stimulation based on internal model principle. We first present the solution of the global robust output regulation problem for output feedback system with nonlinear exosystem. Then we show that the robust control problem for the modified FitzHugh-Nagumo neuron model can be formulated as the global robust output regulation problem and the solvability conditions for the output regulation problem for the modified FitzHugh-Nagumo neuron model are all satisfied. Then we apply the obtained output regulation results to the control problem for modified FitzHugh-Nagumo neuron model. Finally, an output feedback control law is designed for the modified FitzHugh-Nagumo neuron model to achieve global stability of the closed-loop system in the presence of uncertain parameters and external stimulus. An example is shown that the proposed algorithm can completely reject the external electrical stimulation generated from a Van der Pol circuit.},
author = {Jiang, Yuan, Dai, Jiyang},
journal = {Kybernetika},
keywords = {control theory; Lyapunov methods; internal model principle; modified FitzHugh--Nagumo model; Van der Pol circuit; Van der Pol circuit; control theory; Lyapunov methods; internal model principle; modified Fitzhugh-Nagumo model},
language = {eng},
number = {4},
pages = {612-629},
publisher = {Institute of Information Theory and Automation AS CR},
title = {Robust control of chaos in modified FitzHugh-Nagumo neuron model under external electrical stimulation based on internal model principle},
url = {http://eudml.org/doc/196901},
volume = {47},
year = {2011},
}

TY - JOUR
AU - Jiang, Yuan
AU - Dai, Jiyang
TI - Robust control of chaos in modified FitzHugh-Nagumo neuron model under external electrical stimulation based on internal model principle
JO - Kybernetika
PY - 2011
PB - Institute of Information Theory and Automation AS CR
VL - 47
IS - 4
SP - 612
EP - 629
AB - This paper treats the question of robust control of chaos in modified FitzHugh-Nagumo neuron model under external electrical stimulation based on internal model principle. We first present the solution of the global robust output regulation problem for output feedback system with nonlinear exosystem. Then we show that the robust control problem for the modified FitzHugh-Nagumo neuron model can be formulated as the global robust output regulation problem and the solvability conditions for the output regulation problem for the modified FitzHugh-Nagumo neuron model are all satisfied. Then we apply the obtained output regulation results to the control problem for modified FitzHugh-Nagumo neuron model. Finally, an output feedback control law is designed for the modified FitzHugh-Nagumo neuron model to achieve global stability of the closed-loop system in the presence of uncertain parameters and external stimulus. An example is shown that the proposed algorithm can completely reject the external electrical stimulation generated from a Van der Pol circuit.
LA - eng
KW - control theory; Lyapunov methods; internal model principle; modified FitzHugh--Nagumo model; Van der Pol circuit; Van der Pol circuit; control theory; Lyapunov methods; internal model principle; modified Fitzhugh-Nagumo model
UR - http://eudml.org/doc/196901
ER -

References

top
  1. Arcak, M., Kokotovic, P., 10.1016/S0005-1098(01)00160-1, Automatica 37 (2001), 1923–1930. (2001) MR2110678DOI10.1016/S0005-1098(01)00160-1
  2. Byrnes, C. I., Priscoli, F. D., Isidori, A., 10.1016/S0005-1098(96)00184-7, Automatica 33 (1997), 369–385. (1997) Zbl0873.93043MR1442555DOI10.1016/S0005-1098(96)00184-7
  3. Chen, C., Ding, Z., Lennox, B., 10.1109/TCSII.2008.2009962, IEEE Trans. Circuits. Syst. II: Expr. Briefs 55 (2008), 1289–1293. (2008) DOI10.1109/TCSII.2008.2009962
  4. Chen, Z., Huang, J., 10.1109/TAC.2004.841125, IEEE Trans. Automat. Control 50 (2005), 117–121. (2005) MR2110818DOI10.1109/TAC.2004.841125
  5. Chen, Z., Huang, J., 10.1016/j.automatica.2005.03.015, Automatica 41 (2005), 1447–1454. (2005) Zbl1086.93013MR2160490DOI10.1016/j.automatica.2005.03.015
  6. Davison, E. J., 10.1109/TAC.1976.1101137, IEEE Trans. Automat. Control 21 (1976), 25–34. (1976) Zbl0326.93007MR0406616DOI10.1109/TAC.1976.1101137
  7. Desoer, C. A., Lin, C. A., 10.1109/TAC.1985.1104078, IEEE Trans. Automat. Control 30 (1985), 861–867. (1985) Zbl0573.93027MR0799479DOI10.1109/TAC.1985.1104078
  8. Benedetto, M. D. Di, 10.1080/00207178708933784, Internat. J. Control 45 (1987), 1023–1034. (1987) MR0880281DOI10.1080/00207178708933784
  9. Che, Y. Q., Wang, J., Zhou, S. S., Deng, B., Robust synchronization control of coupled chaotic neurons under external electrical stimulation, Chaos Solit. Fract. 40 (2009), 1333–1342. (2009) Zbl1197.37110MR2526117
  10. Ding, Z., 10.1016/S0005-1098(00)00129-1, Automatica 37 (2001), 113–119. (2001) Zbl0964.93057MR1832885DOI10.1016/S0005-1098(00)00129-1
  11. Ding, Z., 10.1109/TAC.2005.864199, IEEE Trans. Automat. Control 51 (2006), 498–503. (2006) MR2205690DOI10.1109/TAC.2005.864199
  12. Ding, Z., Decentralized output regulation of large scale nonlinear systems with delay, Kybernetika. 45 (2009), 33–48. (2009) Zbl1158.93303MR2489579
  13. Isidori, A., Byrnes, C. I., 10.1109/9.45168, IEEE Trans. Automat. Control 35 (1990), 131–140. (1990) Zbl0704.93034MR1038409DOI10.1109/9.45168
  14. Francis, D. A., Wonham, W. M., 10.1007/BF01447855, Appl. Math. Optim. 2 (1975), 170–194. (1975) Zbl0351.93015MR0389331DOI10.1007/BF01447855
  15. Huang, J., Chen, Z., 10.1109/TAC.2004.839236, IEEE Trans. Automat. Control 49 (2004), 2203–2218. (2004) MR2106750DOI10.1109/TAC.2004.839236
  16. Ideker, T., Galitski, T., Hood, L., 10.1146/annurev.genom.2.1.343, Ann. Rev. Genom, Hum. Genet. 2 (2001), 343–372. (2001) DOI10.1146/annurev.genom.2.1.343
  17. Gong, Q., Lin, W., 10.1109/TAC.2003.812804, IEEE Trans. Automat. Control 48 (2003), 1049–1054. (2003) MR1986277DOI10.1109/TAC.2003.812804
  18. Huang, J., 10.1109/9.388697, IEEE Trans. Automat. Control 40 (1995), 1118–1122. (1995) Zbl0829.93027MR1345975DOI10.1109/9.388697
  19. Huang, J., Lin, C-F., 10.1109/9.299646, IEEE Trans. Automat. Control. 39 (1994), 1510–1513. (1994) Zbl0800.93290MR1283933DOI10.1109/9.299646
  20. Huang, J., Rugh, W. J., 10.1016/0005-1098(90)90081-R, Automatica 26 (1990), 963–972. (1990) Zbl0717.93019MR1080983DOI10.1016/0005-1098(90)90081-R
  21. Isidori, A., Nonlinear Control Systems, 3rd eddition. Springer-Verlag, New York 1995. (1995) Zbl0878.93001MR1410988
  22. Johnson, C. D., 10.1109/TAC.1971.1099830, IEEE Trans. Automat. Control. 16 (1971), 635–644. (1971) DOI10.1109/TAC.1971.1099830
  23. Kitano, H., 10.1126/science.1069492, Science 295 (2002), 1662–1664. (2002) DOI10.1126/science.1069492
  24. Kürten, K. E., Clark, J. W., 10.1016/0375-9601(86)90729-2, Phys. Lett. A, 114 (1986), 413–418. (1986) MR0829167DOI10.1016/0375-9601(86)90729-2
  25. Lin, W., Qian, C., 10.1109/TAC.2002.800773, IEEE Trans. Automat. Control. 47 (2002), 1249–1266. (2002) MR1917435DOI10.1109/TAC.2002.800773
  26. Liu, S., Jiang, Y., Liu, P., Rejection of nonharmonic disturbances in nonlinear systems, Kybernetika 46 (2010), 758–798. (2010) Zbl1205.93158MR2778927
  27. Marino, R., Tomei, P., Nonlinear Control Design-Nonlinear, Robust and Adaptive, Prentice Hall, Englewood Cliffs, New York 1994. (1994) 
  28. Mishra, D., Yadav, A., Ray, S., Kalra, P. K., Nonlinear Dynamical Analysis on Coupled Modified FitzHugh–Nagumo Neuron Model, Lecture Notes in Computer Science. Springer Berlin – Heidelberg. 3496 (2005), 95–101. (2005) Zbl1082.68677
  29. Mishra, D., Yadav, A., Ray, S., Kalra, P. K., Controlling synchronization of modified FitzHugh–Nagumo neurons under external electrical stimulation, NeuroQuantology 1 (2006), 50–67. (2006) 
  30. Ramos, L. E., C̆elikovský, S., Kuc̆era, V., 10.1109/TAC.2004.835404, IEEE Trans. Automat. Control 49 (2004), 1737–1742. (2004) MR2091325DOI10.1109/TAC.2004.835404
  31. Rinzel, J., A formal classification of bursting mechanisms in excitable systems, in mathematical topics in population niology, morphogenesis and neurosciences, Lecture Notes in Biomath., Springer–Verlag, New York. 71 (1987), 267–281. (1987) MR0913344
  32. Rehák, B., Čelikovský, S., Ruiz-León, J., Orozco-Mora, J., A comparison of two fem-based methods for the solution of the nonlinear output regulation problem, Kybernetika 45 (2009), 427–444. (2009) Zbl1165.93320MR2543132
  33. Serrani, A., Isidori, A., 10.1016/S0167-6911(99)00099-7, Syst. Control Lett. 39 (2000), 133–139. (2000) Zbl0948.93027MR1826676DOI10.1016/S0167-6911(99)00099-7
  34. Sun, W., Huang, J., Output regulation for a class of uncertain nonlinear systems with nonlinear exosystems and its application, Science in China, Ser. F: Information Sciences 52 (2009), 2172–2179. (2009) Zbl1182.93072MR2566641
  35. Venkatesh, K. V., Bhartiya, S., Ruhela, A., 10.1016/S0014-5793(04)00310-2, FEBS Lett. 563, (2004), 234–240. (2004) DOI10.1016/S0014-5793(04)00310-2
  36. Wang, J., Zhang, T., Deng, B., Synchronization of FitzHugh–Nagumo neurons in external electrical stimulation via nonlinear control, Chaos Solit. Fract. 31 (2007), 30–38. (2007) Zbl1133.92008MR2263262
  37. Xi, Z., Ding, Z., 10.1016/j.automatica.2006.08.011, Automatica 43 (2007), 143–149. (2007) Zbl1140.93462MR2266780DOI10.1016/j.automatica.2006.08.011
  38. Xi, Z., Ding, Z., 10.1049/iet-cta:20060432, IET Control. Theory Appl. 1 (2007), 1504–1511. (2007) MR2350838DOI10.1049/iet-cta:20060432

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