Controlling of Mean Arterial Pressure by Modified PI-ID Controller Based on Two Optimization Algorithms

Controlling of Mean Arterial Pressure by Modified PI-ID Controller Based on Two Optimization Algorithms

Автор: Ekhlaskaram, Rawaa Haamed

Журнал: International Journal of Modern Education and Computer Science @ijmecs

Статья в выпуске: 4 vol.12, 2020 года.

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High blood pressure is one of the diseases that most people suffer from, and it becomes a serious disease when it is not controlled precisely, especially during the surgical procedure. There must be anesthesiologists during the operation to monitor the pressure during the operation. It is not good and expensive, for patient safety and injection of the patient with the required dose, and it accurately requires an intelligent control to control the patient's pressure This paper presents nonlinear control system, to regulate the Mean Arterial Pressure (MAP) system. This controller is designed 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 Bacterial Foraging Optimization (BFO) are considered to optimize the controller parameters. Also nonlinear gain is used in PI-Id controller rather than fixed gain to make the controller much more sensitive to small value of error. Two algorithms applied to the controller to optimize its parameters to compare their results and determine which gives better results. The comparison results show best improvement when using the suggested controller based on SSA Algorithm. the results have no undershot with less (800s) settling time and low error.

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Mean Arterial Pressure, Modified PI-ID Controller, Squirrel Search Algorithm, Bacterial Foraging Optimization

Короткий адрес: https://sciup.org/15017596

IDR: 15017596   |   DOI: 10.5815/ijmecs.2020.04.04

Список литературы Controlling of Mean Arterial Pressure by Modified PI-ID Controller Based on Two Optimization Algorithms

  • A. Luiz, D. E. O. Cavalcanti, and A. L. Maitelli, “Design of an Intelligent Adaptive Drug Delivery System for Arterial Pressure Control,” vol. 10, pp. 704–712, 2015.
  • H. A. Silva, C. P. Leão, and E. A. Seabra, “Parametric Sensitivity Analysis of a Multiple Model Adaptive Predictive Control for Regulation of Mean Arterial Blood Pressure,” vol. 1, no. Icinco, pp. 520–526, 2018.
  • G. C. Sowparnika, V. M. Sivakumar, M. Thirumarimurugan, and S. N. Saranya, “Metaphorical analysis of tuning rules for PI and PID controllers in modeling an automatic drug delivery system to control mean arterial blood pressure,” 2017 4th Int. Conf. Adv. Comput. Commun. Syst. ICACCS 2017, pp. 0–4, 2017.
  • A. A. Basha, S. Vivekanandan, and P. Parthasarathy, “Evolution of blood pressure control identification in lieu of post-surgery diabetic patients: a review,” Heal. Inf. Sci. Syst., vol. 6, no. 1, 2018.
  • J. B. Slate, L. C. Sheppard, V. C. Rideout, and E. H. Blackstone, “Model for Design of a Blood Pressure Controller for Hypertensive Patients.,” J. Sound Vib., vol. 12, no. 8, pp. 285–289, 1979.
  • V. S. Manju and S. Maka, “Design of drug delivery system for blood pressure control,” 2013 Annu. Int. Conf. Emerg. Res. Areas, AICERA 2013 2013 Int. Conf. Microelectron. Commun. Renew. Energy, ICMiCR 2013 - Proc., 2013.
  • Á. Herrero et al., “Preface,” Adv. Intell. Syst. Comput., vol. 239, pp. v–vi, 2014.
  • S. Urooj and B. Singh, “Control of mean arterial pressure using fractional PID controller,” Proc. 10th INDIACom; 2016 3rd Int. Conf. Comput. Sustain. Glob. Dev. INDIACom 2016, pp. 1556–1559, 2016.
  • D. Sathish and A. Nachiappan, “Automatic drug delivery system for the drug adrenaline using Pi, Pid, Imc & Mpc controllers,” Int. J. Recent Technol. Eng., vol. 7, no. 6, pp. 2043–2047, 2019.
  • J. B. Slate and L. C. Sheppard, “Model-Based Adaptive Blood Pressure Controller.,” IFAC Proc. Vol., vol. 2, no. 4, pp. 1437–1442, 1983.
  • S. Saxena and Y. V. Hote, “A simulation study on optimal IMC based PI/PID controller for mean arterial blood pressure,” Biomed. Eng. Lett., vol. 2, no. 4, pp. 240–248, 2012.
  • A. J. Humaidi and I. K. Ibraheem, “Speed Control of Permanent Magnet DC Motor with Friction and Measurement Noise Using Novel Nonlinear Extended State Observer-Based Anti-Disturbance Control,” Energies, vol. 12, no. 9, 2019.
  • H. Hu, L. Zhang, Y. Bai, P. Wang, and X. Tan, “A Hybrid Algorithm Based on Squirrel Search Algorithm and Invasive Weed Optimization for Optimization,” IEEE Access, vol. 7, pp. 105652–105668, 2019.
  • Y. Xiaobing, Y. Xianrui, and C. Hong, “An improved gravitational search algorithm for global optimization,” J. Intell. Fuzzy Syst., vol. 37, no. 4, pp. 5039–5047, 2019.
  • R. Sivakumar, P. Deepa, and D. Sankaran, “A study on BFO algorithm based PID controller design for MIMO process using various cost functions,” Indian J. Sci. Technol., vol. 9, no. 12, pp. 1–6, 2016.
  • S. A. Nirmala, R. Muthu, and B. Veena Abirami, “Model Predictive Control of Drug Infusion System for Mean Arterial Pressure Regulation of Critical Care Patients,” Res. J. Appl. Sci. Eng. Technol., vol. 7, no. 21, pp. 4601–4605, 2014.
  • S. Urooj and B. Singh, “Fractional-order PID control for postoperative mean arterial blood pressure control scheme,” Procedia Comput. Sci., vol. 152, pp. 380–389, 2019.
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