A Combination of Fuzzy Logic and Neural Network Algorithms for Active Vibration Control

1996 ◽  
Vol 210 (3) ◽  
pp. 153-167 ◽  
Author(s):  
S-J Huang ◽  
R-J Lian
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Vibration Control ◽  
Vibration Amplitude ◽  
Mimo System ◽  
Active Vibration Control ◽  
Lumped Mass ◽  
Network Algorithms ◽  
Neural Network Controller ◽  
Active Vibration

The construction of a dynamic absorber incorporating active vibration control is described. The absorber is a 2 degree of freedom spring-lumped mass system sliding on a guide pillar, with two internal vibration disturbance sources. Both the main mass and the secondary absorber mass were acted on by direct current (d.c.) servo motors, respectively, to suppress the vibration amplitude. In this paper, a new control approach is proposed by combining fuzzy logic and neural network algorithms to control the multi-input/multi-output (MIMO) system. Firstly, the fuzzy logic controller was designed for controlling the main influence part of the MIMO system. Secondly, the coupling neural network controller was employed to take care of the coupling effect and refine the control performance of the MIMO system. The experimental results show that the control system effectively suppresses the vibration amplitude and with good position tracking accuracy.

scholarly journals Active Vibration Control of the Smart Plate Using Artificial Neural Network Controller

2015 ◽  
Vol 2015 ◽  
pp. 1-20 ◽  
Author(s):  
Mohit ◽  
Deepak Chhabra ◽  
Suresh Kumar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Vibration Control ◽  
Active Vibration Control ◽  
Cantilever Plate ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Network Controller ◽  
Active Vibration ◽  
Artificial Neural

The active vibration control (AVC) of a rectangular plate with single input and single output approach is investigated using artificial neural network. The cantilever plate of finite length, breadth, and thickness having piezoelectric patches as sensors/actuators fixed at the upper and lower surface of the metal plate is considered for examination. The finite element model of the cantilever plate is utilized to formulate the whole strategy. The compact RIO and MATLAB simulation software are exercised to get the appropriate results. The cantilever plate is subjected to impulse input and uniform white noise disturbance. The neural network is trained offline and tuned with LQR controller. The various training algorithms to tune the neural network are exercised. The best efficient algorithm is finally considered to tune the neural network controller designed for active vibration control of the smart plate.

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.

Active Vibration Control for Lathe Based Neural Network

2011 ◽  
Vol 84-85 ◽  
pp. 183-187 ◽  
Author(s):  
Jin Hua Wang ◽  
Wen Juan Huang ◽  
Hong Yan Zhang ◽  
Yao Gang Li
Keyword(s):  
Neural Network ◽  
Control System ◽  
Vibration Control ◽  
Active Vibration Control ◽  
System Model ◽  
Matlab Simulation ◽  
Model Based ◽  
Active Vibration ◽  
Simulation Results ◽  
Control System Model

In this paper, we took lathe as the research object, and established an active vibration control system model based on neural network AVC (Active Vibration Control) system, and the Matlab simulation results showed that the AVC system can reduce vibration effectively and improve the lathe’s accuracy.

Active vibration control of a hoop truss structure with piezoelectric bending actuators based on a fuzzy logic algorithm

2018 ◽  
Vol 27 (8) ◽  
pp. 085030 ◽  
Author(s):  
Yajun Luo ◽  
Xue Zhang ◽  
Yahong Zhang ◽  
Yuandong Qu ◽  
Minglong Xu ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Vibration Control ◽  
Active Vibration Control ◽  
Truss Structure ◽  
Fuzzy Logic Algorithm ◽  
Active Vibration
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