CONTROLLING THE MEAN ARTERIAL PRESSURE BY MODIFIED MODEL REFERENCE ADAPTIVE CONTROLLER BASED ON TWO OPTIMIZATION ALGORITHMS

Rawaa HAAMED

engrawaa1990@gmail.com
Mustansiriyah University, Computer Engineering Department, Palestine Street, 14022, Baghdad (Iraq)

Ekhlas HAMEED


Mustansiriyah University, Computer Engineering Department, Palestine Street, 14022, Baghdad (Iraq)

Abstract

This paper Presents Modified Model Reference Adaptive Controller (MRAC) to regulate the hight blood pressure. It is based on slate model that represent the mathematical equation that clarifies relationship between blood pressure and vasoactive drug injection. In this work Squirrel Search Algorithm (SSA) and Grey Wolf Optimizer (GWO)algorithms are considered to optimize the controller parameters. the results showed that the suggested controller has good performance and stabilize the mean arterial pressure with small settling time (below than 400s) and small overshoot (below than 1 mmHg) with low amount of error.


Keywords:

Mean Arterial Pressure, Squirrel Search Algorithm, Model Reference Adaptive Controller

Basha, A.A., & Vivekanandan, S. (2019). Enhanced Optimal Insulin Regulation in Post-Operative Diabetic Patients: An Adaptive Cascade Control Compensation-Based Approach With Diabetic and Hypertension. IEEE Access, 7, 90973–90981. https://doi.org/10.1109/ACCESS.2019.2927248
DOI: https://doi.org/10.1109/ACCESS.2019.2927248   Google Scholar

Basha, A.A., Vivekanandan, S., & Parthasarathy, P. (2018). Evolution of blood pressure control identification in lieu of post-surgery diabetic patients: a review. Health Inf Sci Syst, 6, 17. https://doi.org/10.1007/s13755-018-0055-z
DOI: https://doi.org/10.1007/s13755-018-0055-z   Google Scholar

Cavalcanti, A.L., & Maitelli, A.L. (2015). Design of an Intelligent Adaptive Drug Delivery System for Arterial Pressure Control. WSEAS Transactions on Systems and Control, 10, 704–712.
  Google Scholar

da Silva, S.J., Scardovelli, T.A., & da Silva Boschi, S.R.M. et al. (2019). Simple adaptive PI controller development and evaluation for mean arterial pressure regulation. Res. Biomed. Eng., 35, 157–165. https://doi.org/10.1007/s42600-019-00017-y
DOI: https://doi.org/10.1007/s42600-019-00017-y   Google Scholar

de Moura Oliveira, P.B., Durães, J., & Pires, E.J.S. (2014). Mean Arterial Pressure PID Control Using a PSO-BOIDS Algorithm. In: Á. Herrero, et al. (Eds.), International Joint Conference SOCO’13-CISIS’13-ICEUTE’13. Advances in Intelligent Systems and Computing (vol. 239). Springer, Cham
DOI: https://doi.org/10.1007/978-3-319-01854-6_10   Google Scholar

Enbiya, S., Mahieddine, F., & Hossain, A. (2011). Model reference adaptive scheme for multi-drug infusion for blood pressure control. J Integr Bioinform, 8(3), 173. https://doi.org/10.2390/biecoll-jib-2011-173
DOI: https://doi.org/10.1515/jib-2011-173   Google Scholar

Hu, H., Zhang, L., Bai, Y., Wang, P., & Tan, X. (2019). A Hybrid Algorithm Based on Squirrel Search Algorithm and Invasive Weed Optimization for Optimization. IEEE Access, 7, 105652–105668. https://doi.org/10.1109/ACCESS.2019.2932198
DOI: https://doi.org/10.1109/ACCESS.2019.2932198   Google Scholar

Jain, P., & Nigam, M.J. (2013). Design of a Model Reference Adaptive Controller Using Modified MIT Rule for a Second Order System. Adv. Electron. Electr. Eng., 3(4), 477–484.
  Google Scholar

Jones, R.W., & Tham, M.T. (2005). An undergraduate CACSD project: The control of mean arterial blood pressure during surgery. Int. J. Eng. Educ., 21(6) PART I, 1043–1049.
  Google Scholar

Khan, T.A., & Ling, S.H. (2020). An improved gravitational search algorithm for solving an electromagnetic design problem. J Comput Electron, 19, 773–779. https://doi.org/10.1007/s10825-020-01476-8
DOI: https://doi.org/10.1007/s10825-020-01476-8   Google Scholar

Ladaci, S. (2012). Postoperative Blood Pressure Control Using a Fractional order Adaptive Regulator. In 13th International conference on Sciences and Techniques of Automatic control & computer engineering (pp. 254–265). Monastir, Tunisia.
  Google Scholar

Malagutti, N. (2014). Particle filter-based robust adaptive control for closed-loop administration of sodium nitroprusside. J Comput Surg, 1, 8. https://doi.org/10.1186/2194-3990-1-8
DOI: https://doi.org/10.1186/2194-3990-1-8   Google Scholar

Malagutti, N., Dehghani, A., & Kennedy, R.A. (2013). Robust control design for automatic regulation of blood pressure. IET Control Theory Appl., 7(3), 387–396. http://dx.doi.org/10.1049/iet-cta.2012.0254
DOI: https://doi.org/10.1049/iet-cta.2012.0254   Google Scholar

Mirjalili, S., Mirjalili, S.M., & Lewis, A. (2014). Grey Wolf Optimizer. Adv. Eng. Softw., 69, 46–61.
DOI: https://doi.org/10.1016/j.advengsoft.2013.12.007   Google Scholar

Nirmala, S.A., Muthu, R., & Abirami, B.V. (2013). Drug infusion control for mean arterial pressure regulation of critical care patients. In 2013 IEEE International Conference of IEEE Region 10 (TENCON 2013) (pp. 1-4). Xi'an. https://doi.org/10.1109/TENCON.2013.6718882.
DOI: https://doi.org/10.1109/TENCON.2013.6718882   Google Scholar

Precup, R.-E., David, R.-C., Szedlak-Stinean, A.-I., Petriu, E.M., & Dragan, F. (2017). An Easily Understandable Grey Wolf Optimizer and Its Application to Fuzzy Controller Tuning. Algorithms, 10, 68.
DOI: https://doi.org/10.3390/a10020068   Google Scholar

Saeed Al-Khayyt, S. (2017). Tuning PID Controller by Neural Network for Robot Manipulator Trajectory Tracking. Al-Khwarizmi Engineering Journal, 9(1), 19-28.
  Google Scholar

Saxena, S., & Hote, Y.V. (2012). A simulation study on optimal IMC based PI/PID controller for mean arterial blood pressure. Biomed. Eng. Lett., 2, 240–248. https://doi.org/10.1007/s13534-012-0077-4
DOI: https://doi.org/10.1007/s13534-012-0077-4   Google Scholar

Silva, H.A., Leão, C.P., & Seabra, E.A. (2018). Parametric Sensitivity Analysis of a Multiple Model Adaptive Predictive Control for Regulation of Mean Arterial Blood Pressure. In Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2018) (vol. 1, 510–516). http://dx.doi.org/10.5220/0006909805100516
DOI: https://doi.org/10.5220/0006909805100516   Google Scholar

Singh, B., & Urooj, S. (2019). Blood pressure control by deterministic learning based fuzzy logic control. Int. J. Eng. Adv. Technol., 8(3), 6–10.
  Google Scholar

Slate, J.B., & Sheppard, L.C. (1983). Model-Based Adaptive Blood Pressure Controller. IFAC Proc. Vol., 2(4), 1437–1442.
DOI: https://doi.org/10.1016/S1474-6670(17)63200-2   Google Scholar

Urooj, S., & Singh, B. (2019). Fractional-order PID control for postoperative mean arterial blood pressure control scheme. Procedia Comput. Sci., 152, 380–389. https://doi.org/10.1016/j.procs.2019.05.002
DOI: https://doi.org/10.1016/j.procs.2019.05.002   Google Scholar

Download


Published
2020-06-30

Cited by

HAAMED, R., & HAMEED, E. (2020). CONTROLLING THE MEAN ARTERIAL PRESSURE BY MODIFIED MODEL REFERENCE ADAPTIVE CONTROLLER BASED ON TWO OPTIMIZATION ALGORITHMS. Applied Computer Science, 16(2), 53–67. https://doi.org/10.23743/acs-2020-12

Authors

Rawaa HAAMED 
engrawaa1990@gmail.com
Mustansiriyah University, Computer Engineering Department, Palestine Street, 14022, Baghdad Iraq

Authors

Ekhlas HAMEED 

Mustansiriyah University, Computer Engineering Department, Palestine Street, 14022, Baghdad Iraq

Statistics

Abstract views: 206
PDF downloads: 26


License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

All articles published in Applied Computer Science are open-access and distributed under the terms of the Creative Commons Attribution 4.0 International License.


Similar Articles

<< < 4 5 6 7 8 9 10 11 12 13 > >> 

You may also start an advanced similarity search for this article.