Passivity-based neural network adaptive output feedback control for nonlinear nonnegative dynamical systems
Passivity-based neural network adaptive output feedback control for nonlinear nonnegative dynamical systems
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DOI：
10.1109/tnn.2004.841782
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发表时间：
2005-03
影响因子：
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通讯作者：
T. Hayakawa;W. Haddad;J. Bailey;N. Hovakimyan
中科院分区：
文献类型：
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作者：
T. Hayakawa;W. Haddad;J. Bailey;N. Hovakimyan
一般来说，自适应神经网络控制在药理学，特别是麻醉和重症监护病房医学方面的潜在临床应用是显而易见的。具体来说，在手术中监测和控制麻醉的深度是特别重要的。非阴性和室室模型为包括临床药理学在内的生物和生理系统提供了一个广泛的框架，并且非常适合于开发药物给药闭环控制模型。针对非线性不确定非负区隔系统的自适应设定点调节问题，提出了一种神经网络自适应输出反馈控制框架。该框架是基于lyapunov的，保证了物理系统状态和神经网络加权增益对应的误差信号的最终有界性。该方法适用于具有未知维数未建模动力学的非线性非负系统，并保证在非负初始条件下物理系统状态保持在状态空间的非负正交。最后，提供了一个数值例子，涉及麻醉药物咪达唑仑的输注，以保持非心脏手术麻醉深度的期望恒定水平，以证明所提出的方法的有效性。
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 nonnegative 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.