Abstract
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 non-negative quantities between subsystems or compartments wherein each compartment is assumed to be kinetically homogeneous. In this paper, we develop a 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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 561-566 |
| Number of pages | 6 |
| Journal | Proceedings of the American Control Conference |
| Volume | 1 |
| State | Published - Nov 6 2003 |
| Externally published | Yes |
| Event | 2003 American Control Conference - Denver, CO, United States Duration: Jun 4 2003 → Jun 6 2003 |
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ASJC Scopus subject areas
- Electrical and Electronic Engineering
Cite this
Neural Network Adaptive Control for Nonlinear Nonnegative Dynamical Systems. / Hayakawa, Tomohisa; Haddad, Wassim M.; Hovakimyan, Naira; Chellaboina, Vijay Sekhar.
In: Proceedings of the American Control Conference, Vol. 1, 06.11.2003, p. 561-566.Research output: Contribution to journal › Conference article
}
TY - JOUR
T1 - Neural Network Adaptive Control for Nonlinear Nonnegative Dynamical Systems
AU - Hayakawa, Tomohisa
AU - Haddad, Wassim M.
AU - Hovakimyan, Naira
AU - Chellaboina, Vijay Sekhar
PY - 2003/11/6
Y1 - 2003/11/6
N2 - 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 non-negative quantities between subsystems or compartments wherein each compartment is assumed to be kinetically homogeneous. In this paper, we develop a 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.
AB - 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 non-negative quantities between subsystems or compartments wherein each compartment is assumed to be kinetically homogeneous. In this paper, we develop a 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.
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M3 - Conference article
AN - SCOPUS:0142217170
VL - 1
SP - 561
EP - 566
JO - Proceedings of the American Control Conference
JF - Proceedings of the American Control Conference
SN - 0743-1619
ER -