@inproceedings{6a2248092b704f46b30aab2e7a4c3455,
title = "Robust adaptive control scheme using Hopfield dynamic neural network for nonlinear nonaffine systems",
abstract = "In this paper, we propose a robust adaptive control scheme using Hopfield-based dynamic neural network for uncertain or ill-defined nonlinear nonaffine systems. A Hopfield-based dynamic neural network is used to approximate the unknown plant nonlinearity. The robust adaptive controller is designed to achieve a L2 tracking performance to stabilize the closed-loop system. 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, and the tracking error is bounded. The proposed control scheme is applied to control an anti-lock braking system, and the 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 = "adaptive control, Hopfield-based dynamic neural network, Lyapunov stability theory, robust control",
author = "Chen, {Pin Cheng} and Lin, {Ping Zing} and Chi-Hsu Wang and Lee, {Tsu Tian}",
year = "2010",
month = jul,
day = "14",
doi = "10.1007/978-3-642-13318-3_62",
language = "English",
isbn = "3642133177",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
number = "PART 2",
pages = "497--506",
booktitle = "Advances in Neural Networks - ISNN 2010 - 7th International Symposium on Neural Networks, ISNN 2010, Proceedings",
edition = "PART 2",
note = "null ; Conference date: 06-06-2010 Through 09-06-2010",
}