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

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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