Major impediment to developing a control methodology for controlling the depth of anesthesia (DOA) is the design of a robust control strategy which provides precise input tracking, to be robust versus inter-intra patient's variability and external disturbances. In this study, we proposed a new robust strategy which incorporates an auto-tuning neuron into the adaptive fuzzy sliding mode control (AFSMC) for controlling the DOA. In this study fuzzy systems are applied to approximate the system

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... rtainties. The main obstacle of conventional sliding mode control is chattering problem. To address this problem, we combined AFSMC with neural control. AFSMC acts as a main controller and forces the system state toward the sliding surface. Neural controller acts as an axillary controller and when the state of the system closes into the boundary layer, AFSMC is replaced by the neural controller. The stability study of the proposed strategy is performed by using the Lyapunov stability theory. Pharmacokinetic-Pharmacodynamic model has been used as a model of patients. Control performance of AFSMC strategy has been compared with fuzzy logic controller (FLC). Simulation results on 8 patients show that the proposed approach achieves a reliable controller with accurate tracking of input signal in the presence of patient variability and external disturbance. Compared to previous studies our proposed strategy has several advantages such as shorter settling time, elimination of overdosing and under dosing, and generating a smooth control input signal without intense switching action.