An adaptive multi-critic neuro-fuzzy control framework for intravenous anesthesia administration

Development of closed-loop control strategies for drug infusion and delivery is one of the most recent efforts of control engineers and clinicians. This can be used especially in anesthesia during different surgeries to stabilize the patient in the desired awareness condition. In this paper, an adaptive neuro-fuzzy controller is proposed to overcome the current challenges in closed-loop control of anesthesia such as inter and intra patient variability, complex and nonlinear dynamics, measurement noises and surgical disturbances as well as the presence of undershoot and overshoot in the induction phase. The results show an acceptable performance of the proposed controller in solving these problems by providing a learning scheme