Neural Network Adaptive Control for Nonlinear Nonnegative Dynamical Systems

Tomohisa Hayakawa, Wassim M. Haddad, Naira Hovakimyan, Vijay Sekhar Chellaboina

Research output: Contribution to journalConference article

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 languageEnglish (US)
Pages (from-to)561-566
Number of pages6
JournalProceedings of the American Control Conference
Volume1
StatePublished - Nov 6 2003
Externally publishedYes
Event2003 American Control Conference - Denver, CO, United States
Duration: Jun 4 2003Jun 6 2003

Fingerprint

Nonlinear dynamical systems
Neural networks
Energy balance
Dynamical systems
Controllers

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 journalConference article

Hayakawa, Tomohisa ; Haddad, Wassim M. ; Hovakimyan, Naira ; Chellaboina, Vijay Sekhar. / Neural Network Adaptive Control for Nonlinear Nonnegative Dynamical Systems. In: Proceedings of the American Control Conference. 2003 ; Vol. 1. pp. 561-566.
@article{f8f3772bda44416eb62f3b013b99bfcb,
title = "Neural Network Adaptive Control for Nonlinear Nonnegative Dynamical Systems",
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.",
author = "Tomohisa Hayakawa and Haddad, {Wassim M.} and Naira Hovakimyan and Chellaboina, {Vijay Sekhar}",
year = "2003",
month = "11",
day = "6",
language = "English (US)",
volume = "1",
pages = "561--566",
journal = "Proceedings of the American Control Conference",
issn = "0743-1619",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

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.

UR - http://www.scopus.com/inward/record.url?scp=0142217170&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0142217170&partnerID=8YFLogxK

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 -