A Review of Modern Control Strategies for Clinical Evaluation of Propofol Anesthesia Administration Employing Hypnosis Level Regulation

Table 4

Comparative analysis of various control mechanisms and clinical tools employed in anesthesia.

Terminology	Action	Merits	Demerits	References
Anesthesia	Lack of sense	Applied in surgical procedure	Effect digestive system, vomiting, and so forth	[1–4, 6, 8–11, 15, 18–21, 23, 25, 35, 36]
Propofol	Anesthetic agent	Fast metabolic action, less side effects, being easily recoverable	No	[1, 2, 4–7, 9, 11, 13, 16, 18–23, 28]
Remifentanil	Analgesic, painkiller	Less side effect, providing relief from pain, no postoperative effect	Excessive amount affects the stomach	[17–19, 24]
Nitrous oxide	Inhale volatile drugs	Used as painkiller	Not purely hypnotic	[1, 16, 23]
PID controller	Linear control technique	Fast transient response, showing adaptive behavior	Linearizing the data leads to loss of information. Cannot cope with uncertainties	[9, 18, 30–32, 34, 35, 37–40]
Sliding mode control	Nonlinear control scheme	Handling uncertainties like skin incision, less steady error up to 5%	Chattering is observed in hypnosis level	[40, 41]
Adaptive fuzzy SMC	Robust control scheme	Handling chattering in maintenance phase of anesthesia	Steady-state error still exists	[42–46]
Model predicative control	Optimal control strategy	Noise rejection of Intense care equipment, hypnosis level tracking	Settling time of achieving hypnosis can further be improved; steady-state error is 5%	[15, 47, 48]
Robust predicative control	Robust control scheme	Handling interpatient and intrapatient variability	No serious issues. Result is clinically accepted	[47]
Backstepping control	Nonlinear control algorithm	Fast transient response	Steady-state error exists	[49, 50]
Internal model control	Robust control scheme	Handling dynamics in hypnosis level	Complication in handling uncertainty	[51, 52]
Adaptive control	Used in adaptive model	Handling interpatient variability	Complex mathematics involved	[36, 47, 53–55]
control	Based on linear model	Handling uncertainly	Data lost in linearizing model	[36, 56]
TCI	Open loop system	Being easily applicable	Unable to compensate disturbances	[1, 5, 27, 57, 58]
BIS	Display cortical activity of brain	Extracting the inform of DOH from EEG easily	Unable to compensate noise of other equipment in ICU	[5, 8–10, 16, 18, 19, 22, 25, 26, 34]
TANGO	Software platform	Supervisory network for sensing as well as control purpose	Not viable for compensating interpatient variability	[59–61]