Abstract:
Anesthesia infusion has major role in regulating and maintaining the depth of hypnosis
of a patient during surgeries. The idea of anesthesia is to move the patient to an
unconscious state and make the surgery painless. For regulating depth of hypnosis of a
patient during surgical procedures, many linear control algorithms have been introduced
but they are not good enough to deal with disturbances and uncertainties present in the
system. In this research work, we have proposed optimized conditioned super twisting
sliding mode controller to get the desired Bi-spectral Index Scale (BIS) value and to
improve the system’s overall performance as well as to remove chattering. Moreover,
the controller is robust against inter-patient variability dynamics and the surgical stimuli
that help in moving the patient towards conscious state. For regulation of hypnosis level
of a patient, the reference value of BIS is generated through a trained Artificial Neural
Network. The real set of data of 8 patients undergoing surgery is used to examine the
controller’s performance. Lyapunov stability criterion is used for ensuring stability of the
system. MATLAB/Simulink ODE 45 environment is used for verifying the performance
of proposed controller. The proposed controller is then compared with already proposed
super twisting sliding mode controller (STSMC). The simulation results demonstrate
the improved dynamic behavior and robustness with conditioned STSMC. Real-time
hardware-in-loop (HIL) testing is used to further verify the system’s performance.
