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
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
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.
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
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
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
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
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.
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.
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
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.
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
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
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
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
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
Saeed Al-Khayyt, S. (2017). Tuning PID Controller by Neural Network for Robot Manipulator Trajectory Tracking. Al-Khwarizmi Engineering Journal, 9(1), 19-28.
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
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
Singh, B., & Urooj, S. (2019). Blood pressure control by deterministic learning based fuzzy logic control. Int. J. Eng. Adv. Technol., 8(3), 6–10.
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
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