  This paper proposes an adaptive neuro-fuzzy framework to improve drug
infusion rate in closed-loop control of anesthesia. The proposed controller
provides a sub-optimal propofol administration rate as input to reach the
desired bispectral index, which is the output of the system, in both induction
and maintenance phases. In this controller, a critic agent assesses the plant
output and produces a reinforcement signal to adapt the controller parameters
and minimize the propofol administration rate. The controller is applied to a
conventional pharmacokinetic-pharmacodynamics model of anesthesia to evaluate
its applicability in closed loop-control of anesthesia. To simulate the
designed controller, physiological parameters of 12 patients are used in the
mathematical model. The simulation results show that the proposed controller
can overcome current challenges in the closed-loop control of anesthesia like
inter patient variability, model uncertainties and surgical disturbances
without overdose or underdose in a time range of 2 to 4 minutes. Analytical
comparison of results shows the strength of the controller in closed-loop
control of anesthesia.
