title: Passivity-based neural network adaptive output feedback control for nonlinear nonnegative dynamical systems.
abstract: The potential clinical applications of adaptive neural network 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 adaptive set-point regulation of nonlinear uncertain nonnegative and compartmental systems. The proposed framework is Lyapunov-based and guarantees ultimate boundedness of the error signals corresponding to the physical system states and the neural network weighting gains. The approach is applicable to nonlinear nonnegative systems with unmodeled dynamics of unknown dimension and guarantees that the physical system states remain in the nonnegatiye orthant of the state-space for nonnegative initial conditions. Finally, a numerical example involving the infusion of the anesthetic drug midazolam for maintaining a desired constant level of depth of anesthesia for noncardiac surgery is provided to demonstrate the efficacy of the proposed approach.
authors.affiliation: Japan Science and Technology Agency, Saitama 332-0012, Japan. tomohisa_hayakawa@ipc.i.u-tokyo.ac.jp
authors.forename: Tomohisa
authors.initials: T
authors.lastname: Hayakawa
authors.forename: Wassim M
authors.initials: WM
authors.lastname: Haddad
authors.forename: James M
authors.initials: JM
authors.lastname: Bailey
authors.forename: Naira
authors.initials: N
authors.lastname: Hovakimyan
mesh_terms: D000222:Adaptation, Physiological / Q000502:physiology; D005246:Feedback / Q000502:physiology; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics