Anesthesia is a medically induced state of unconsciousness, loss of sensation, or analgesia (lack of pain). This state is achieved through the administration of anesthetic drugs, primarily during surgical procedures to render the patient unconscious and alleviate pain. This study focuses on intravenous anesthesia, where anesthetic drugs are directly injected into a patient's vein. Anesthesiologists not only induce anesthesia but also maintain the patient's physiological stability, ensuring adequate oxygenation. Automated drug delivery systems provide a more precise and efficient method for controlling anesthesia, enhancing patient care. These systems streamline the anesthesia process, allowing anesthesiologists to focus on other critical aspects of surgery while minimizing drug administration. Studies have shown that automated systems more accurately regulate the depth of anesthesia and require lower drug doses compared to traditional methods. Three core elements are essential for constructing these devices: measurement, modeling, and control. Measurement involves quantifying the depth of anesthesia using indices such as the Bispectral Index (BIS). Modeling involves creating a mathematical representation of how a patient's body responds to a given drug dose. Finally, the controller determines the drug infusion rate. Various control algorithms have been explored for regulating the level of unconsciousness, including PID controllers, model predictive controllers, and adaptive controllers. This research aims to design and improve control systems for precise administration of the anesthetic drug propofol. Initially, A simple yet effective PID controller has been designed for this purpose, and its performance is eva‎luated under various conditions. Considering the importance of the controller's performance in the presence of uncertainties and its independence from the patient model parameters, a direct model reference adaptive controller (non-normalized) is developed. Both designed controllers utilize the inverse of the Hill function to address the nonlinear aspects of the patient model, which requires precise information about the patient model.Consequently, in the next section of this research, a new approach is proposed by linearly approximating the Hill function. Since this results in a non-minimum phase system, a direct model reference adaptive controller with a different control law is designed for this system, replacing it with a tracking error. The average percentage errors for the designed controllers were found to be 0.67%, 1.91%, and 0.74%, respectively. Additionally, the PID controller consumed more propofol during anesthesia compared to the adaptive controllers.In the final section of the research, considering that slower heart pumping and drug exchange during anesthesia may cause delays, an adaptive direct model reference controller with a different control law is designed to address this challenge.

فرزانه شايق بروجني , مرضيه كمالي

رسول امير فتاحي ورنوسفادراني , احسان روحاني