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Development of an Integrated Hybrid Model of Human Cardiovascular and Respiratory System for Control Study purposes
Kehtari, Payam | 2013
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- Type of Document: M.Sc. Thesis
- Language: Farsi
- Document No: 44348 (06)
- University: Sharif University of Technology
- Department: Chemical and Petroleum Engineering
- Advisor(s): Bozorgmehri, Ramin
- Abstract:
- Mathematical models are used for describing physical systems to make it possible to study and investigate the behavior of these systems and compare their performance and improve them. For this reason, a mathematical model for cardiovascular system and respiratory system is used to study how these two systems interact. The cardiovascular system model and the respiratory system model comprise four and two compartments, respectively. In fact, the combined model contains the left and right ventricles, body tissue, lung, systemic blood circulation, and pulmonary compartments. Heart rate and alveolar ventilation are assumed to be the quantities through them the central nervous system controls the arterial blood pressure and the blood gas concentrations. Different methods are used for imitating, approximating, or approaching to the body control system. In this regard, optimal control and model predictive control methods are used and their performance have been compared. The obtained results show that MPC algorithm is more robust and is able to compensate the effect of loads disturbing the system, where as the optimal control method is not able to do so. Hence, it seems that MPC algorithm is closer to the respiratory control algorithm. In order to extend the model to consider the detail states of the respiratory system the model used for cardiovascular repiratory system has been merged to the detail respiratory model proposed by Ben Tal and the obtained model has been validated qualitatively. However, the extended model should be quantitatively validated to make it as a reliable bench mark of the Respiratory control analysis
- Keywords:
- Respiratory System ; Heart Beat ; Cardiovascular System ; Model Predictive Control ; Optimal Control ; Systemic Blood Circulation ; Arterial Blood Pressure
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