access icon free Flatness-based control approach to drug infusion for cardiac function regulation

A new control method based on differential flatness theory is developed in this study, aiming at solving the problem of regulation of haemodynamic parameters. Actually control of the cardiac output (volume of blood pumped out by heart per unit of time) and of the arterial blood pressure is achieved through the administered infusion of cardiovascular drugs such as dopamine and sodium nitroprusside. Time delays between the control inputs and the system's outputs are taken into account. Using the principle of dynamic extension, which means that by considering certain control inputs and their derivatives as additional state variables, a state-space description for the heart's function is obtained. It is proven that the dynamic model of the heart is a differentially flat one. This enables its transformation into a linear canonical and decoupled form, for which the design of a stabilising feedback controller becomes possible. The proposed feedback controller is of proven stability and assures fast and accurate tracking of the reference setpoints by the outputs of the heart's dynamic model. Moreover, by using a Kalman filter-based disturbances’ estimator, it becomes possible to estimate in real-time and compensate for the model uncertainty and external perturbation inputs that affect the heart's model.

Inspec keywords: drug delivery systems; haemodynamics; feedback; cardiovascular system; volume control; blood; drugs; medical control systems; Kalman filters; controllers; blood pressure measurement; blood vessels; perturbation theory

Other keywords: flatness-based control; feedback controller; cardiac function regulation; linear canonical form; drug 5 infusion; cardiac output; system outputs; sodium nitroprusside; haemodynamic parameters; time delays; reference setpoints; Kalman filter-based disturbances estimator; arterial blood pressure; external perturbation inputs; state-space description; state variables; dynamic extension; cardiovascular drugs; control inputs; differential flatness theory; blood pumped volume; dopamine; decoupled form

Subjects: Controllers; Level, flow and volume control; Haemodynamics, pneumodynamics; Patient diagnostic methods and instrumentation; Pressure and vacuum measurement; Patient care and treatment; Biomedical measurement and imaging; Biological and medical control systems; Patient care and treatment

References

    1. 1)
      • 25. Fliess, M., Mounier, H.: ‘Tracking control and πat-freeness of infinite dimensional linear systems’. In Picci, G., Gilliam, D.S. (EDs): ‘Dynamical systems, control, coding and computer vision’ (Birkhaüser, Basel-Switzerland, 1993), vol. 258, pp. 4168.
    2. 2)
      • 24. Lévine, J.: ‘Analysis and control of nonlinear systems: a flatness-based approach’ (Springer, Berlin-Heidelberg, 2009).
    3. 3)
      • 21. Rudolph, J.: ‘Flatness based control of distributed parameter systems, examples and computer exercises from various technological domains’ (Shaker Verlag, Aachen, 2003).
    4. 4)
      • 6. Malagutti, N., Dehghani, A., Kennedy, R.A.: ‘Improved robust performance in a system for automatic administration of vasoactive drugs’. Biosignals 2012 – Proc. of the Int. Conf. on Bio-Inspired Systems and Signal Processing, Algarve, Portugal, 2012, pp. 282290.
    5. 5)
      • 16. Sprunk, A., Garcia, M., Schreiber, U., et al: ‘Cardiovascular model for development’, Hangzhou, China, September 2011, vol. 38, pp. 153156.
    6. 6)
    7. 7)
      • 20. Rigatos, G.G.: ‘Nonlinear control and filtering using differential flatness approaches: applications to electromechanical systems’ (Springer, Berlin-Heidelberg, 2015).
    8. 8)
      • 19. Rigatos, G.G.: ‘Advanced models of neural networks: nonlinear dynamics and stochasticity in biological neurons’ (Springer, Berlin-Heidelberg, 2013).
    9. 9)
    10. 10)
      • 18. Rigatos, G.G.: ‘Modelling and control for intelligent industrial systems: adaptive algorithms in robotics and industrial engineering’ (Springer, Berlin-Heidelberg, 2011).
    11. 11)
      • 13. Barney, E.H., Kaufman, H.: ‘Model reference adaptive control of cardiac output and blood pressure through two-drug infusions’. Fifth IEEE Int. Conf. on Intelligent Control, Philadelphia, USA, September 1990, vol. 2, pp. 739744.
    12. 12)
    13. 13)
      • 3. Gopinath, R.S., Bequettelt, B.W., Roy, R.J., et al: ‘Multirate MPC design for a nonlinear drug infusion system’. Proc. of the American Control Conf., Baltimore, MD, USA, June 1994.
    14. 14)
    15. 15)
    16. 16)
      • 23. Lévine, J.: ‘On necessary and sufficient conditions for differential flatness, applicable algebra in engineering’, Commun. Comput., Springer, 2011, 22, (1), pp. 4790.
    17. 17)
    18. 18)
      • 4. Boldisor, C.N., Comnac, V., Coman, S., et al: ‘A combined experience and model based design methodology of a fuzzy control system for mean arterial pressure and cardiac output’. 18th IFAC World Congress Milano, Italy, August–September 2011.
    19. 19)
      • 11. Enbiya, S., Mahieddine, F., Hossain, A.: ‘Model reference adaptive scheme for multi-drug infusion for blood pressure control’, J. Integrative Bioinf., 2011, 8, (3), p. 173.
    20. 20)
      • 22. Sira-Ramirez, H., Agrawal, S.: ‘Differentially flat systems. (Marcel Dekker, New York, 2004).
    21. 21)
    22. 22)
    23. 23)
      • 32. Basseville, M., Nikiforov, I.: ‘Detection of abrupt changes: theory and applications’ (Prentice-Hall, New Jersey, 1993).
    24. 24)
    25. 25)
      • 10. Palerm, C., Bequette, B.W., Ozcelik, S.: ‘Robust control of drug infusion with time delays using direct adaptive control: experimental results’. Proc. of the American Control Conf., Chicago, Illinois, June 2000.
    26. 26)
    27. 27)
    28. 28)
    29. 29)
    30. 30)
      • 12. Enbiya, S., Saleh, H., Hossein, A., et al: ‘Multi-drug infusion control using model reference adaptive algorithm’. InRocha, M., Corchado, J., Fdez Riverola, F., , Valencia, A. (EDs): ‘Advances in intelligent and soft computing’ (Springer, Berlin, 2011), pp. 141148.
    31. 31)
    32. 32)
    33. 33)
      • 7. Malagutti, N.: ‘Particle filter-based robust adaptive control for closed-loop administration of sodium nitroprusside’, J. Comput. Surg., Springer, 2014, 1, (8), pp. 119.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-syb.2016.0012
Loading

Related content

content/journals/10.1049/iet-syb.2016.0012
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading