TY - JOUR
T1 - Neural network adaptive output feedback control for intensive care unit sedation and intraoperative anesthesia
AU - Haddad, Wassim M.
AU - Bailey, James M.
AU - Hayakawa, Tomohisa
AU - Hovakimyan, Naira
N1 - Funding Information:
Manuscript received December 1, 2005; revised September 20, 2006; accepted February 5, 2007. This work was supported in part by the National Science Foundation under Grant ECS-0601311 and by the U.S. Air Force Office of Scientific Research under Grants FA9550-06-1-0240 and F49620-03-1-0443.
PY - 2007/7
Y1 - 2007/7
N2 - The potential applications of neural adaptive control for pharmacology, in general, and anesthesia and critical care unit medicine, in particular, are clearly apparent. Specifically, monitoring and controlling the depth of anesthesia in surgery is of particular importance. Nonnegative and compartmental models provide a broad framework for biological and physiological systems, including clinical pharmacology, and are well suited for developing models for closed-loop control of drug administration. In this paper, we develop a neural adaptive output feedback control framework for nonlinear uncertain nonnegative and compartmental systems with nonnegative control inputs. The proposed framework is Lyapunov-based and guarantees ultimate boundedness of the error signals. In addition, the neural adaptive controller guarantees that the physical system states remain in the nonnegative orthant of the state space. Finally, the proposed approach is used to control the infusion of the anesthetic drug propofol for maintaining a desired constant level of depth of anesthesia for noncardiac surgery.
AB - The potential applications of neural adaptive control for pharmacology, in general, and anesthesia and critical care unit medicine, in particular, are clearly apparent. Specifically, monitoring and controlling the depth of anesthesia in surgery is of particular importance. Nonnegative and compartmental models provide a broad framework for biological and physiological systems, including clinical pharmacology, and are well suited for developing models for closed-loop control of drug administration. In this paper, we develop a neural adaptive output feedback control framework for nonlinear uncertain nonnegative and compartmental systems with nonnegative control inputs. The proposed framework is Lyapunov-based and guarantees ultimate boundedness of the error signals. In addition, the neural adaptive controller guarantees that the physical system states remain in the nonnegative orthant of the state space. Finally, the proposed approach is used to control the infusion of the anesthetic drug propofol for maintaining a desired constant level of depth of anesthesia for noncardiac surgery.
KW - Adaptive control
KW - Automated anesthesia
KW - Bispectral index (BIS)
KW - Dynamic output feedback
KW - Electroencephalography
KW - Neural networks
KW - Nonlinear nonnegative systems
KW - Nonnegative control
KW - Set-point regulation
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U2 - 10.1109/TNN.2007.899164
DO - 10.1109/TNN.2007.899164
M3 - Article
C2 - 17668661
AN - SCOPUS:34547092430
VL - 18
SP - 1049
EP - 1066
JO - IEEE Transactions on Neural Networks
JF - IEEE Transactions on Neural Networks
SN - 1045-9227
IS - 4
ER -