Experimental Study on Active Vibration Control of a Single-Link Flexible Manipulator Using Tools of Fuzzy Logic and Neural Networks

2005 ◽  
Vol 54 (3) ◽  
pp. 1200-1208 ◽  
Author(s):  
A. Jnifene ◽  
W. Andrews
Keyword(s):  
Neural Networks ◽  
Experimental Study ◽  
Fuzzy Logic ◽  
Vibration Control ◽  
Active Vibration Control ◽  
Flexible Manipulator ◽  
Single Link ◽  
Active Vibration

Experimental Study on Active Vibration Control of Flexible Manipulator Based on Improved PPF Algorithm

2014 ◽  
Vol 989-994 ◽  
pp. 2774-2777
Author(s):  
Fei Du ◽  
Tian Bing Ma
Keyword(s):  
Experimental Study ◽  
Vibration Control ◽  
Active Vibration Control ◽  
Flexible Manipulator ◽  
Experimental Result ◽  
Work Efficiency ◽  
Control Effect ◽  
Positioning Accuracy ◽  
Active Vibration ◽  
Modal Vibration

The positioning accuracy and work efficiency of flexible manipulator is seriously affected by its vibration. Therefore, vibration control of single joint flexible manipulator is studied by using an improved PPF algorithm based on LabVIEW and piezoelectric technology. Firstly, the improved PPF algorithm principle is introduced. Then, the experiment process is discussed in detail. Experimental result shows that the improved PPF algorithm can effectively control the first two modal vibration of flexible manipulator. The control effect is close to 14 dB and improved nearly 4.5 dB compared with PPF algorithm.

Theoretical Investigation of a Structure for Active Vibration Control with Fuzzy Logic Approach

2014 ◽  
Vol 598 ◽  
pp. 529-533
Author(s):  
Erdi Gülbahçe ◽  
Mehmet Çelik ◽  
Mustafa Tinkir
Keyword(s):  
Mathematical Model ◽  
Fuzzy Logic ◽  
Vibration Control ◽  
Modal Analysis ◽  
Prediction Models ◽  
Block Diagram ◽  
Active Vibration Control ◽  
Control Block ◽  
Inference System ◽  
Active Vibration

The main purpose of this study is to prepare mathematical model for active vibration control of a structure. This paper presents the numerical and experimental modal analysis of aluminum cantilever beam in order to investigate the dynamic characteristics of the structure. The results will be used for active vibration control of structure’s experimental setup. Experimental natural frequencies are obtained and compared to verify the proposed numerical model by using modal analysis results. MATLAB System Identification Toolbox and ANSYS harmonic response function are used together to estimate beam’s equations of motion which include its amplitude, frequency and phase angle values. Moreover, the mathematical model of beam is simulated in MATLAB/Simulink software to determine the dynamic behavior of the proposed system. Furthermore, another prediction model approach with multiple input and single output is used to find the realistic behavior of beam via an adaptive neural-network-based fuzzy logic inference system, in addition, impulse responses of the proposed models are compared and the control block diagram for active vibration control is implemented. As a first iteration, PID type controller is designed to suppress vibrations against the disturbance input. The results of modal analysis, the prediction models, controlled and uncontrolled system responses are presented in graphics and tables for obtaining a sample numerical active vibration control.

scholarly journals Experimental implementation of artificial neural network-based active vibration control & chatter suppression

2021 ◽  
Author(s):  
Yong Xia
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Control System ◽  
Vibration Control ◽  
Control Strategies ◽  
Active Vibration Control ◽  
Chatter Suppression ◽  
Active Vibration ◽  
Artificial Neural

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.

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