Open AccessNeural Network Adaptive Control for Discrete-Time Nonlinear Nonnegative Dynamical Systems
Author(s) -
Wassim M. Haddad,
VijaySekhar Chellaboina,
Qing Hui,
Tomohisa Hayakawa
Publication year - 2008
Publication title -
advances in difference equations
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.67
H-Index - 51
eISSN - 1687-1847
pISSN - 1687-1839
DOI - 10.1155/2008/868425
Subject(s) - artificial neural network , nonlinear system , control theory (sociology) , discrete time and continuous time , adaptive control , computer science , control (management) , mathematics , artificial intelligence , physics , statistics , quantum mechanics
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 they 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 neuroadaptive 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 neuroadaptive controller guarantees that the physical system states remain in the nonnegative orthant of the state space for nonnegative initial conditions