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Intelligent Learning Algorithms for Active Vibration Control

Madkour, A.; Hossain, M. A.; Dahal, K. P.; Yu, H.


A. Madkour

M. A. Hossain

K. P. Dahal


This correspondence presents an investigation into the comparative performance of an active vibration control (AVC) system using a number of intelligent learning algorithms. Recursive least square (RLS), evolutionary genetic algorithms (GAs), general regression neural network (GRNN), and adaptive neuro-fuzzy inference system (ANFIS) algorithms are proposed to develop the mechanisms of an AVC system. The controller is designed on the basis of optimal vibration suppression using a plant model. A simulation platform of a flexible beam system in transverse vibration using a finite difference method is considered to demonstrate the capabilities of the AVC system using RLS, GAs, GRNN, and ANFIS. The simulation model of the AVC system is implemented, tested, and its performance is assessed for the system identification models using the proposed algorithms. Finally, a comparative performance of the algorithms in implementing the model of the AVC system is presented and discussed through a set of experiments.

Journal Article Type Article
Online Publication Date Aug 20, 2007
Publication Date 2007-09
Deposit Date Jun 15, 2022
Journal IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews)
Print ISSN 1094-6977
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 37
Issue 5
Pages 1022-1033
Keywords Adaptive systems, fuzzy neural network, intelligent control, recursive estimation, system identification, vibration control
Public URL