Nonlinear Robust Adaptive Sliding Mode Control Strategy for Innate Immune Response to Influenza Virus

REFERENCES

• ‘World Health Organization (WHO). Influenza virus infections in humans’, February 2014.
   Google Scholar
• H. Schulenburg, C. Kurz, and J. Ewbank, “Evolution of the innate immune system: The worm perspective,” Immunol. Rev., Vol. 198, pp. 36–58, 2004.
   PubMed Web of Science ®Google Scholar
• E. W. Larson, J. W. Dominik, A. H. Rowberg, and G. A. Higbee, “Influenza virus population dynamics in the respiratory tract of experimentally infected mice’,” Infect. Immun., Vol. 13, no. 2, pp. 438–447, 1976.
   PubMed Web of Science ®Google Scholar
• P. Baccam, C. Beauchemin, C. A. Macken, F. G. Hayden, and A. S. Perelson, “Kinetics of influenza A virus infection in humans,” J. Virol., Vol. 80, no. 15, pp. 7590–7599, 2006.
   PubMed Web of Science ®Google Scholar
• A. Boianelli, V. K. Nguyen, T. Ebensen, K. Schulze, E. Wilk, N. Sharma, and M. Meyer-Hermann, “Modeling influenza virus infection: A roadmap for influenza research,” Viruses, Vol. 7, no. 10, pp. 5274–5304, 2015.
   PubMed Web of Science ®Google Scholar
• H. Y. Lee, et al., “Simulation and prediction of the adaptive immune response to influenza a virus infection,” J. Virol., Vol. 83, no. 14, pp. 7151–7165, 2009.
   PubMed Web of Science ®Google Scholar
• B. Hancioglu, D. Swigon, and G. Clermont, “A dynamical model of human immune response to influenza a virus infection,” J. Theor. Biol., Vol. 246, no. 1, pp. 70–86, 2007.
   PubMed Web of Science ®Google Scholar
• S. Almocera, A. E. Nguyen, V. K. Hernandez-Vargas, and E. A, “Multiscale model within-host and between-host for viral infectious diseases,” J. Math. Biol., Vol. 77, pp. 1035–1057, 2017.
   Web of Science ®Google Scholar
• Z. Abbasi, I. Zamani, A. H. Amiri Mehra, and M. Shafieirad, “Optimal control of SEIR epidemic model considering nonlinear transmission rate and time delay,” in 2021 7th International Conference on Control, Instrumentation and Automation (ICCIA), IEEE, 2021.
   Google Scholar
• Z. Abbasi, I. Zamani, A. H. Amiri Mehra, A. Ibeas, and M. Shafieirad, “Optimal allocation of vaccine and antiviral drugs for influenza containment over delayed multi-scale epidemic model considering time-dependent transmission rate,” Comput. Math. Methods. Med., Vol. 2021, pp. 1–27, 2021.
   Google Scholar
• A. H. Amiri Mehra, M. Shafieirad, Z. Abbasi, and I. Zamani, “Parameter estimation and prediction of COVID-19 epidemic turning point and ending time of a case study on SIR/SQAIR epidemic models,” Comput. Math. Methods. Med., Vol. 2020, pp. 1–13, 2020.
   Web of Science ®Google Scholar
• A. H. Amiri Mehra, M. Shafieirad, Z. Abbasi, I. Zamani, and Z. Aarabi, “Fuzzy sliding mode controller design and analysis of an SQEIAR epidemic model for COVID-19 to determine the quarantine rate,” Journal of Control, Vol. 14, no. 5, pp. 59–70, 2021.
   Google Scholar
• A. E. S. Almocera, G. Quiroz, and E. A. Hernandez-Vargas, “Stability analysis in COVID-19 within-host model with immune response,” Commun. Nonlinear Sci. Numer. Simul., Vol. 95, pp. 105584, 2021.
   PubMed Web of Science ®Google Scholar
• Z. Abbasi, M. Shafieirad, A. H. Amiri Mehra, and I. Zamani, “Optimized ANFIS-based control design using genetic algorithm to obtain the vaccination and isolation rates for the COVID-19,” in 2021 29th Iranian Conference on Electrical Engineering (ICEE), IEEE, 2021.
   Google Scholar
• A. H. Amiri Mehra, M. Shafieirad, Z. Abbasi, and I. Zamani, “Stability analysis of a New switched SEIAR-Vac-Iso epidemic model for the COVID-19,” in 2021 29th Iranian Conference on Electrical Engineering (ICEE), IEEE, 2021.
   Google Scholar
• S. Chankan, R. Thienchai, and W. Yukunthorn, “Control theory for HIV dynamics: sliding mode control in antiviral drug therapy,” In Journal of Physics: Conference Series, Vol. 1144, pp. 012070, 2018.
   Google Scholar
• A. J. Anelone, Y. Orlov, and S. K. Spurgeon, “Modelling the self-tolerance mechanisms of T cells: An adaptive sliding mode control approach,” in 2014 UKACC International Conference on Control, IEEE, 2014, pp. 573–578.
   Google Scholar
• O. Aghajanzadeh, M. Sharifi, S. Tashakori, and H. Zohoor, “Robust adaptive lyapunov-based control of hepatitis B infection,” IET Syst. Biol., Vol. 12, no. 2, pp. 62–67, 2018.
   PubMed Web of Science ®Google Scholar
• O. Aghajanzadeh, M. Sharifi, S. Tashakori, and H. Zohoor, “Nonlinear adaptive control method for treatment of uncertain hepatitis B virus infection,” Biomed. Signal. Process. Control., Vol. 38, pp. 174–181, 2017.
   Web of Science ®Google Scholar
• P. Rokhforoz, A. A. Jamshidi, and N. N. Sarvestani, “Adaptive robust control of cancer chemotherapy with extended kalman filter observer,” Informatics in Medicine Unlocked, Vol. 8, pp. 1–7, 2017.
   Google Scholar
• A. E. Hernandez-Vargas, and M. Meyer-Hermann, “Innate immune system dynamics to influenza virus,” IFAC Proceedings Volumes, Vol. 45, no. 18, pp. 260–265, 2012.
   Google Scholar
• R. F. Stengel, R. Ghigliazza, N. Kulkarni, and O. Laplace, “Optimal control of innate immune response,” Optimal Control Applications and Methods, Vol. 23, no. 2, pp. 91–104, 2002.
   Web of Science ®Google Scholar
• Z. Abbasi, I. Zamani, A. H. Amiri Mehra, M. Shafieirad, and A. Ibeas, “Optimal control design of impulsive SQEIAR epidemic models with application to COVID-19,” Chaos, Solitons Fractals, Vol. 139, pp. 110054, 2020.
   PubMed Web of Science ®Google Scholar
• I. Zamani, A. H. Amiri Mehra, Z. Abbasi, and M. Shafiee, “Robust stability for affine TS fuzzy impulsive control systems subject to parametric uncertainties,” in 2019 27th Iranian Conference on Electrical Engineering (ICEE), IEEE, 2019, pp. 1102–1107.
   Google Scholar
• A. T. Azar, and A. E. Hassanien. Modeling, control and drug development for COVID-19 outbreak prevention. Switzerland, AG: Springer, 2022.
   Google Scholar
• A. Ibeas, M. de la Sen, and S. Alonso-Quesada, “Robust sliding control of SEIR epidemic models,” Math. Probl. Eng., Vol. 2014, pp. 1–12, 2014.
   Google Scholar
• H. Moradi, M. Sharifi, and G. Vossoughi, “Adaptive robust control of cancer chemotherapy in the presence of parametric uncertainties: A comparison between three hypotheses,” Comput. Biol. Med., Vol. 56, pp. 145–157, 2015.
   PubMed Web of Science ®Google Scholar
• A. H. Amiri Mehra, I. Zamani, Z. Abbasi, and A. Ibeas, “Observer-Based adaptive PI sliding mode control of developed uncertain SEIAR influenza epidemic model considering dynamic population,” J. Theor. Biol., Vol. 482, pp. 109984, 2019.
   PubMed Web of Science ®Google Scholar
• A. Ibeas, M. de la Sen, S. Alonso-Quesada, I. Zamani, and M. Shafiee, “Observer design for SEIR discrete-time epidemic models,” in 13th International Conference on Control Automation Robotics & Vision (ICARCV), IEEE, 2014, pp. 1321–1326.
   Google Scholar
• A. Ibeas, M. Sen, S. Alonso-Quesada, and I. Zamani, “Stability analysis and observer design for discrete-time SEIR epidemic models,” Adv. Differ. Equ., Vol. 2015, no. 1, pp. 122, 2015.
   Google Scholar
• M. Sharifi, A. Jamshidi, and N. N. Sarvestani, “An adaptive robust control strategy in A cancer tumor-immune system under uncertainties,” IEEE/ACM Trans. Comput. Biol. Bioinf., Vol. 16, pp. 865–873, 2018.
   Web of Science ®Google Scholar
• G. Rimmelzwaan, M. Baars, E. Claas, and A. Osterhaus, “Comparison of RNA hybridization, hemagglutination assay, titration of infectious virus and immunofluorescence as methods for monitoring influenza virus replication in vitro,” J. Virol. Methods, Vol. 74, no. 1, pp. 57–66, 1998.
   PubMed Web of Science ®Google Scholar
• C. A. Beauchemin, and A. Handel, “A review of mathematical models of influenza a infections within a host or cell culture: lessons learned and challenges ahead,” BMC Public Health, Vol. 11, no. 1, pp. 1–15, 2011.
   PubMedGoogle Scholar
• J.-J. E. Slotine, and W. Li. Applied nonlinear control. Englewood Cliffs, NJ: Prentice Hall, 1991.
   Google Scholar
• L. Möhler, D. Flockerzi, H. Sann, and U. Reichl, “Mathematical model of influenza a virus production in large-scale microcarrier culture,” Biotechnol. Bioeng., Vol. 90, no. 1, pp. 46–58, 2005.
   PubMed Web of Science ®Google Scholar
• R. A. Saenz, et al., “Dynamics of influenza virus infection and pathology,” J. Virol., Vol. 84, no. 8, pp. 3974–3983, 2010.
   PubMed Web of Science ®Google Scholar
• V. A. Kuznetsov, I. A. Makalkin, M. A. Taylor, and A. S. Perelson, “Nonlinear dynamics of immunogenic tumors: parameter estimation and global bifurcation analysis,” Bull. Math. Biol., Vol. 56, no. 2, pp. 295–321, 1994.
   PubMed Web of Science ®Google Scholar
• M. Quinlivan, et al., “Pro-Inflammatory and antiviral cytokine expression in vaccinated and unvaccinated horses exposed to equine influenza virus,” Vaccine, Vol. 25, no. 41, pp. 7056–7064, 2007.
   PubMed Web of Science ®Google Scholar