Adaptive high-order Hopfield-based neural network tracking controller for uncertain nonlinear dynamical system

Chi-Hsu Wang*, Kun Neng Hung

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The Hopfield neural network (HNN) has been widely discussed for controlling a nonlinear dynamical system. The weighting factors in HNN will be tuned via the Lyapunov stability criterion to guarantee the convergence performance. The proposed architecture in this paper is high-order Hopfield-based neural network (HOHNN), in which additional inputs from functional link net for each neuron are considered. Compared to HNN, the HOHNN performs faster convergence rate. The simulation results for both HNN and HOHNN show the effectiveness of HOHNN controller for affine nonlinear system. It is obvious from the simulation results that the performance for HOHNN controller is better than HNN controller.

Original languageEnglish
Title of host publication2010 International Conference on Networking, Sensing and Control, ICNSC 2010
Pages382-387
Number of pages6
DOIs
StatePublished - 9 Jun 2010
Event2010 International Conference on Networking, Sensing and Control, ICNSC 2010 - Chicago, IL, United States
Duration: 10 Apr 201012 Apr 2010

Publication series

Name2010 International Conference on Networking, Sensing and Control, ICNSC 2010

Conference

Conference2010 International Conference on Networking, Sensing and Control, ICNSC 2010
CountryUnited States
CityChicago, IL
Period10/04/1012/04/10

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