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- Type of Document: M.Sc. Thesis
- Language: Farsi
- Document No: 41911 (06)
- University: Sharif University of Technology
- Department: Chemical and Petroleum Engineering
- Advisor(s): Shahrokhi, Mohammad
- Abstract:
- Anesthesia control during surgeries has been greatly considered in recent years. In this thesis nonlinear and linear control algorithems are implemented to control the human anesthesia system. First, non-adaptive linear model predictive algorithem is tested. Since the model parameters change from one person to another, adaptive algorithms seem more suitable for the system. Therfore, adaptive linear predictive control is implemented in the next step, and it shows a better performance. According to the system nonlinearities, non-adaptive nonlinear model predictive control (NMPC) based on the state-space model is also applied to the system and it has a better performance index. The main problem of this controller is its weakness against model mismatch. To solve this problem, adaptive and non-adaptive model predictive control based on the Wiener model are implemented, and it has been shown that they have a better performance compared to non-adaptive NMPC. Moreover, non-adaptive sliding-mode control is applied to the system. It decreases the hypnosys time dramatically, but fast controller actions, makes it difficult to design a practical controller. Since, all the states are necessary for the controller implementation an observer has been designed and used for this purpose and closed loop stability has been shown. Furthermore, in the literature an adaptive state-feedback control of anesthesia process has been proposed. Since for implementation of this scheme process states are required, a reduced order observer has been designed and stability of the closed loop system in presence of observer has been stablished. Finally, all of the above controllers including a PI controller are compared, and the best controller is chosen
- Keywords:
- Predictive Control ; Sliding Mode Control ; Anesthesia Control ; Full-Order Observer ; Reduced-Order Observer
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