Intelligent Learning Algorithms for Active Vibration Control

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

Abstract

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.

Citation

Madkour, A., Hossain, M. A., Dahal, K. P., & Yu, H. (2007). Intelligent Learning Algorithms for Active Vibration Control. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 37(5), 1022-1033. https://doi.org/10.1109/tsmcc.2007.900640

• 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
• DOI: https://doi.org/10.1109/tsmcc.2007.900640
• Keywords: Adaptive systems, fuzzy neural network, intelligent control, recursive estimation, system identification, vibration control
• Public URL: http://researchrepository.napier.ac.uk/Output/2879313