Control Strategies for Active Vibration Control via Twin Rotor Damper

2015 ◽  
Vol 105 (17) ◽  
pp. 1-8
Author(s):  
Richard Bäumer ◽  
Uwe Starossek
Keyword(s):  
Vibration Control ◽  
Control Strategies ◽  
Active Vibration Control ◽  
Active Vibration ◽  
Twin Rotor

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.

Distributed active vibration cooperative control for flexible structure with multiple autonomous substructure model

2020 ◽  
Vol 26 (21-22) ◽  
pp. 2026-2036
Author(s):  
Xiangdong Liu ◽  
Haikuo Liu ◽  
Changkun Du ◽  
Pingli Lu ◽  
Dongping Jin ◽  
...  
Keyword(s):  
Vibration Control ◽  
Cooperative Control ◽  
Vibration Suppression ◽  
Control Strategies ◽  
Active Vibration Control ◽  
Flexible Structures ◽  
Lyapunov Theory ◽  
Sensors And Actuators ◽  
Active Vibration ◽  
Distributed Cooperative Control

The objective of this work was to suppress the vibration of flexible structures by using a distributed cooperative control scheme with decentralized sensors and actuators. For the application of the distributed cooperative control strategy, we first propose the multiple autonomous substructure models for flexible structures. Each autonomous substructure is equipped with its own sensor, actuator, and controller, and they all have computation and communication capabilities. The primary focus of this investigation was to illustrate the use of a distributed cooperative protocol to enable vibration control. Based on the proposed models, we design two novel active vibration control strategies, both of which are implemented in a distributed manner under a communication network. The distributed controllers can effectively suppress the vibration of flexible structures, and a certain degree of interaction cooperation will improve the performance of the vibration suppression. The stability of flexible systems is analyzed by the Lyapunov theory. Finally, numerical examples of a cantilever beam structure demonstrate the effectiveness of the proposed methods.

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

2021 ◽  
Author(s):  
Yong Xia
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Vibration Control ◽  
Control Strategies ◽  
Active Vibration Control ◽  
Experimental System ◽  
Experimental Tests ◽  
Development Environment ◽  
Active Vibration ◽  
Artificial Neural

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.

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

2021 ◽  
Author(s):  
Yong Xia
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Vibration Control ◽  
Control Strategies ◽  
Active Vibration Control ◽  
Experimental System ◽  
Experimental Tests ◽  
Development Environment ◽  
Active Vibration ◽  
Artificial Neural

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.

Adaptive active vibration suppression of flexible beam structures

2008 ◽  
Vol 222 (3) ◽  
pp. 357-364 ◽  
Author(s):  
Y Xia ◽  
A Ghasempoor
Keyword(s):  
Vibration Control ◽  
Time Delays ◽  
Vibration Suppression ◽  
Control Strategies ◽  
Linear Optimization ◽  
Active Vibration Control ◽  
Flexible Beam ◽  
Active Vibration Suppression ◽  
Active Vibration ◽  
Suppression System

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. Although passive vibration control techniques are generally less complex, there is a limit to their effectiveness. Active vibration control strategies, on the other hand, can be very effective but require more complex algorithms and are especially susceptible to time delays. The current paper introduces a novel vibration suppression system using non-linear optimization. The proposed methodology eliminates the need for a feedback loop and the sensitivity to time delays. The system has been evaluated experimentally and the results show the validity of the proposed methodology.

Active Control of Rear Sub-Frame Vibration in Rear and All-Wheel Drive Vehicles

2016 ◽  
Author(s):  
J. Deng ◽  
A. R. Kashani
Keyword(s):  
Vibration Control ◽  
Active Control ◽  
Control Strategies ◽  
Active Vibration Control ◽  
Laboratory Evaluation ◽  
Advantages And Disadvantages ◽  
Gear Mesh ◽  
Wheel Drive ◽  
Active Vibration ◽  
Control Application

Feedback control of the rear sub-frame structure is used to abate its gear mesh induced vibration. The goal of the active control is to absorb vibration at a location close to the perturbation source, i.e., the rear differential. Proof mass actuators (PMAs) are used in this active vibration control application. A tuned absorption-based as well as a linear quadratic active vibration control schemes, each with its own advantages and disadvantages, were developed for this application. Following to the synthesis and numerical simulation of the two active vibration control strategies, they were first evaluated on a test structure in the laboratory. Following the laboratory evaluation, one of the active vibration control strategies was implemented on an all-wheel drive vehicle. Two small PMAs, mounted on the rear sub-frame of the vehicle, were used as the active elements in this vibration control application. An accelerometer placed next to each actuator was used as the feedback sensor. The effectiveness of active vibration control in absorbing the shaker induced vibration of the sub-frame was successfully demonstrated. In addition, rolling dynamometer tests showed effective vibration reduction of rear differential induced vibration of the sub-frame. As expected, lowering the sub-frame vibration resulted in lower vibration and noise in the cabin.

Semi-active vibration control of an 8x8 armored wheeled platform

2016 ◽  
Vol 24 (2) ◽  
pp. 283-302 ◽  
Author(s):  
MW Trikande ◽  
NK Karve ◽  
R Anand Raj ◽  
VV Jagirdar ◽  
R Vasudevan
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Vibration Control ◽  
Ride Comfort ◽  
Control Strategies ◽  
Active Vibration Control ◽  
Active Suspension ◽  
Fire Control ◽  
Ride Control ◽  
Active Vibration

This study proposes skyhook and fuzzy logic based semi-active control strategies to isolate sprung mass motions of 8x8 military vehicle and provide ride quality, road holding and firing accuracy for a platform, removing the passivity constraints of semi-active suspension system. The governing differential equations of motion of 8x8 platform for semi-active vibration control are formulated analytically and validated under multi body dynamics environment. Sprung mass acceleration and displacement are measured on a quarter car set up experimentally to assess the efficacy of skyhook and fuzzy logic controllers. Control strategies, viz. continuous skyhook control, cascade loop control and cascade loop with ride control are implemented. Cascade loop with ride control is employed such that the outer loop stabilizes heave, pitch and roll motions of full vehicle whereas the inner loop, through fuzzy controller, isolates vehicle from uneven disturbances. Various parametric studies are also performed with 8x8 semi-active suspension systems in terms of stochastic road inputs to represent cross country terrain profile. Furthermore, effect of proposed strategies on ride comfort, road holding, amplitude and settling time of vehicle body motions after firing large projectile from gun and aiming accuracy of the fire control system are investigated. It is demonstrated that cascade loop with ride control in semi-active mode improves vehicle ride comfort and road holding and accuracy of fire control system and rate of fire of gun.

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