scholarly journals Dynamic Modelling of a Flexible Beam Structure Using Feedforward Neural Networks for Active Vibration Control

Author(s):  
Tuan A. Z. Rahman ◽  
A. As’arry ◽  
N. A. Abdul Jalil ◽  
R. Kamil

Active vibration control (AVC) techniques show promising results to reduce unwanted vibration level of flexible structures at any desired location. In this paper, the application of non-parametric identification method using feedforward neural networks (FNNs) to model a flexible beam structure for AVC system is presented. An experimental study was carried out to collect input-output dataset of a flexible beam system. The flexible beam was excited using a pseudo-random binary sequence (PRBS) force signal before acquiring the dynamic response of the system. A non-parametric modelling approach of the system was proposed based on feed-forward neural networks (FNNs) while its weight and bias parameters were optimised using chaotic-enhanced stochastic fractal search (SFS) algorithm. The performance of modified SFS algorithm to train a nonlinear auto-regressive exogenous model (NARX) structure FNNs-based model of the system was then compared with its predecessor and with several well-known metaheuristic algorithms. Correlation tests were used to validate the obtained model. Based on the proposed method, a small mean squared error value has been achieved in the validation phase. Considering both convergence rate and result accuracy simultaneously, the chaotic modified SFS algorithm performs significantly better than other training algorithms. In conclusion, the development of a non-parametric model of the flexible beam structure was conducted and validated for future investigations on active vibration control techniques.

Author(s):  
M A Hossain ◽  
M O Tokhi

This paper presents an investigation into the development of an adaptive active control mechanism for vibration suppression using genetic algorithms (GAs). GAs are used to estimate the adaptive controller characteristics, where the controller is designed on the basis of optimal vibration suppression using the plant model. This is realized by minimizing the prediction error of the actual plant output and the model output. A MATLAB GA toolbox is used to identify the controller parameters. A comparative performance of the conventional recursive least-squares (RLS) scheme and the GA is presented. The active vibration control system is implemented with both the GA and the RLS schemes, and its performance assessed in the suppression of vibration along a flexible beam structure in each case.


2021 ◽  
Author(s):  
Yong Xia

Vibration control strategies strive to reduce the effect of harmful vibrations such as machining chatter. In general, these strategies are classified as passive or active. While passive vibration control techniques are generally less complex, there is a limit to their effectiveness. Active vibration control strategies, which work by providing an additional energy supply to vibration systems, on the other hand, require more complex algorithms but can be very effective. In this work, a novel artificial neural network-based active vibration control system has been developed. The developed system can detect the sinusoidal vibration component with the highest power and suppress it in one control cycle, and in subsequent cycles, sinusoidal signals with the next highest power will be suppressed. With artificial neural networks trained to cover enough frequency and amplitude ranges, most of the original vibration can be suppressed. The efficiency of the proposed methodology has been verified experimentally in the vibration control of a cantilever beam. Artificial neural networks can be trained automatically for updated time delays in the system when necessary. Experimental results show that the developed active vibration control system is real time, adaptable, robust, effective and easy to be implemented. Finally, an experimental setup of chatter suppression for a lathe has been successfully implemented, and the successful techniques used in the previous artificial neural network-based active vibration control system have been utilized for active chatter suppression in turning.


Author(s):  
Lawrence R. Corr ◽  
William W. Clark

Abstract This paper presents a numerical study in which active and hybrid vibration confinement is compared with a conventional active vibration control method. Vibration confinement is a vibration control technique that is based on reshaping structural modes to produce “quiet areas” in a structure as opposed to adding damping as in conventional active or passive methods. In this paper, active and hybrid confinement is achieved in a flexible beam with two pairs of piezoelectric actuators and sensors and with two vibration absorbers. For comparison purposes, active damping is achieved also with two pairs of piezoelectric actuators and sensors using direct velocity feedback. The results show that both approaches are effective in controlling vibrations in the targeted area of the beam, with direct velocity feedback being slightly more cost effective in terms of required power. When combined with passive confinement, however, each method is improved with a significant reduction in required power.


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