Hayakawa Tomohisa, Haddad Wassim M, Hovakimyan Naira, Chellaboina VijaySekhar
Japan Science and Technology Agency, Saitama 332-0012, Japan.
IEEE Trans Neural Netw. 2005 Mar;16(2):399-413. doi: 10.1109/TNN.2004.841791.
Nonnegative and compartmental dynamical system models are derived from mass and energy balance considerations that involve dynamic states whose values are nonnegative. These models are widespread in engineering and life sciences and typically involve the exchange of nonnegative quantities between subsystems or compartments wherein each compartment is assumed to be kinetically homogeneous. In this paper, we develop a full-state feedback neural adaptive 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. In addition, the neural adaptive controller guarantees that the physical system states remain in the nonnegative orthant of the state-space for nonnegative initial conditions.
非负和隔室动态系统模型源自质量和能量平衡考虑，这些考虑涉及值为非负的动态状态。这些模型在工程和生命科学中广泛存在，通常涉及子系统或隔室之间非负量的交换，其中每个隔室被假定为动力学上均匀的。在本文中，我们为非线性不确定非负和隔室系统的自适应设定点调节开发了一种全状态反馈神经自适应控制框架。所提出的框架基于李雅普诺夫方法，并保证与物理系统状态和神经网络权重增益相对应的误差信号的最终有界性。此外，神经自适应控制器保证对于非负初始条件，物理系统状态保持在状态空间的非负卦限内。
