Neural network adaptive output feedback control for intensive care unit sedation and intraoperative anesthesia.
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.
School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0150, USA. wm.haddad@aerospace.gatech.edu
Wassim M
WM
Haddad
James M
JM
Bailey
Tomohisa
T
Hayakawa
Naira
N
Hovakimyan
D000758:Anesthesia / Q000379:methods; D003198:Computer Simulation; D003422:Critical Care / Q000379:methods*; D004360:Drug Therapy, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000187:drug effects*; D005103:Expert Systems; D005246:Feedback; D006993:Hypnotics and Sedatives / Q000008:administration & dosage*; D008954:Models, Biological; D016343:Monitoring, Intraoperative / Q000379:methods*; D016571:Neural Networks, Computer
D006993:Hypnotics and Sedatives