Experimental implementation of an artificial neural network-based active vibration control

Vibration Control ◽

Control Strategies ◽

Experimental System ◽

Experimental Tests ◽

Development Environment ◽

Active Vibration ◽

Vibration control strategies strive to reduce the effect of harmful vibrations on machinery and people. 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, on the other hand, require more complex algorithms but can be very effective. In this current work, a novel active vibration control experimental system, including the hardware setup and software development environment, has been successfully implemented. A static artificial neural network-based active vibration control system has been designed and tested based on the experimental system. The artificial neural network is trained to model the plant using a backpropagation algorithm. After training, the network model is used as part of a feedforward controller. the efficiency of this controller is shown through experimental tests.

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

10.32920/ryerson.14663340.v1 ◽

2021 ◽

Author(s):

Yong Xia

Keyword(s):

Neural Network ◽

Neural Networks ◽

Control System ◽

Vibration Control ◽

Control Strategies ◽

Chatter Suppression ◽

Active Vibration ◽

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.

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

10.32920/ryerson.14663340 ◽

2021 ◽

Author(s):

Yong Xia

Keyword(s):

Neural Network ◽

Neural Networks ◽

Control System ◽

Vibration Control ◽

Control Strategies ◽

Chatter Suppression ◽

Active Vibration ◽

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.

106 Gain scheduling type active vibration control with an artificial neural network

The Proceedings of Conference of Hokuriku-Shinetsu Branch ◽

10.1299/jsmehs.2016.53._106-1_ ◽

2016 ◽

Vol 2016.53 (0) ◽

pp. _106-1_-_106-5_

Author(s):

Yu SATOMURA ◽

Kazuhiko HIRAMOTO

Keyword(s):

Neural Network ◽

Vibration Control ◽

Gain Scheduling ◽

Active Vibration ◽

Research on Active Vibration Control Based on Artificial Neural Network

10.1109/isdea.2014.178 ◽

2014 ◽

Author(s):

Wang Hongbo

Keyword(s):

Neural Network ◽

Vibration Control ◽

Active Vibration ◽

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

Advances in Aerospace Engineering ◽

10.1155/2015/137068 ◽

2015 ◽

Vol 2015 ◽

pp. 1-20 ◽

Cited By ~ 1

Author(s):

Mohit ◽

Deepak Chhabra ◽

Suresh Kumar

Keyword(s):

Neural Network ◽

Vibration Control ◽

Cantilever Plate ◽

Neural Network Controller ◽

The Neural Network ◽

Network Controller ◽

Active Vibration ◽

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.