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Neural network adaptive control for discrete-time nonlinear nonnegative dynamical systems | IEEE Conference Publication | IEEE Xplore

Neural network adaptive control for discrete-time 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. Th...Show More

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 nonnegative 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 discrete-time 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.
Date of Conference: 09-12 December 2003
Date Added to IEEE Xplore: 15 March 2004
Print ISBN:0-7803-7924-1
Print ISSN: 0191-2216
Conference Location: Maui, HI, USA

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