Fractional-Order Control Strategy for Anesthesia–Hemodynamic Stabilization in Patients Undergoing Surgical Procedures

Abstract

Fractional calculus has been opening new doors in terms of better modeling and control of several phenomena and processes. Biomedical engineering has seen a lot of combined attention from clinicians, control engineers and researchers in their attempt to offer individualized treatment. A large number of medical procedures require anesthesia, which in turn requires a closely monitored and controlled level of hypnosis, analgesia and neuromuscular blockade, as well maintenance of hemodynamic variables in a safe range. Computer-controlled anesthesia has been given a tremendous amount of attention lately. Hemodynamic stabilization via computer-based control is also a hot topic. However, very few studies on automatic control of combined anesthesia–hemodynamic systems exist despite the fact that hemodynamics is strongly influenced by hypnotic drugs, while the depth of hypnosis is affected by drugs used in hemodynamic control. The very first multivariable fractional-order controller is developed in this paper for the combined anesthesia–hemodynamic system. Simulation studies on 24 patients show the effectiveness of the proposed approach.

Development, Validation, and Comparison of a Novel Nociception/Anti-Nociception Monitor against Two Commercial Monitors in General Anesthesia

“…The clinical practice uses standardized monitors for model-based computerized management of the respective drugs. The control algorithms range from anything in between feedforward, feedback, and predictive-based methodologies [9][10][11][12]. Most popular solutions in the literature are the proportional-integral-derivative (PID) control [13][14][15] and model-based predictive control (MPC) [16][17][18][19][20][21].…”

Modified fractional order PID structure for non‐integer model bioreactor control

“…[9] Fractional calculus, an extension of classical calculus to non-integer orders, has applications in numerous disciplines, such as electromechanical systems, biological systems, and industrial processes. [10][11][12][13] The novel properties of fractional order controllers, such as memory and non-locality, have enabled the development of more robust and efficient control strategies, making them a prospective research area. [14] In recent years, fractional-order control methods have gained significant attention for their versatility and applicability across various engineering domains.…”

Parametric Modeling and Deep Learning for Enhancing Pain Assessment in Postanesthesia

“…Hence, mathematical models give new opportunities for the design and evaluation of closedloop anesthesia controllers [5]. Multiple control strategies have been proposed in literature for anesthesia [6], [7]. However, modeling pain pathways did not capture the main focus of the systems and control community until only recently, the witness being the scarce literature reports on the topic: merely 31 relevant articles [8].…”