@inbook{68179352d48c44f89eecb528348ba580,
title = "Indirect adaptive control using hopfield-based dynamic neural network for SISO nonlinear systems",
abstract = "In this paper we propose an indirect adaptive control scheme using Hopfield-based dynamic neural network for SISO nonlinear systems with external disturbances. Hopfield-based dynamic neural networks are used to obtain uncertain function estimations in an indirect adaptive controller, and a compensation controller is used to suppress the effect of approximation error and disturbance. The weights of Hopfield-based dynamic neural network are on-line tuned by the adaptive laws derived in the sense of Lyapunov, so that the stability of the closed-loop system can be guaranteed. In addition, the tracking error can be attenuated to a desired level by selecting some parameters adequately. Simulation results illustrate the applicability of the proposed control scheme. The designed parsimonious structure of the Hopfield-based dynamic neural network makes the practical implementation of the work in this paper much easier.",
keywords = "Dynamic neural network, Hopfield-based dynamic neural network, Indirect adaptive control, Lyapunov stability theory",
author = "Chen, {Ping Cheng} and Chi-Hsu Wang and Lee, {Tsu Tian}",
year = "2009",
month = oct,
day = "26",
doi = "10.1007/978-3-642-03633-0_11",
language = "English",
isbn = "9783642036323",
series = "Studies in Computational Intelligence",
pages = "189--204",
editor = "J. Fodor and Janusz Kacprzyk",
booktitle = "Aspects of Soft Computing, Intelligent Robotics and Control",
}