Patent
Active acoustic and structural vibration control without online controller adjustment and path modeling
James Ting-Ho Lo,Lei Yu +1 more
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TLDR
Active vibration control (AVC) systems without online path modeling and controller adjustment are provided in this paper that are able to adapt to an uncertain operating environment by using an adaptive recursive neural network whose weights are determined in an offline training and are fixed online during the operation of the system.Abstract:
Active vibration control (AVC) systems without online path modeling and controller adjustment are provided that are able to adapt to an uncertain operating environment. The controller (250, 280, 315, 252, 282, 317, 254, 319) of such an AVC system is an adaptive recursive neural network whose weights are determined in an offline training and are held fixed online during the operation of the system. AVC feedforward, feedback, and feedforward-feedback systems in accordance with the present invention are described. An AVC feedforward system has no error sensor and an AVC feedback system has no reference sensor. All sensor outputs of an AVC system are processed by the controller for generating control signals to drive at least one secondary source (240). While an error sensor (480, 481) must be a vibrational sensor, a reference sensor (230, 270, 295, 305, 330) may be either a vibrational or nonvibrational sensor. The provided AVC systems reduce or eliminate most of such shortcomings of the prior-art AVC systems as use of an error sensor, relatively slow convergence of a weight/waveform adjustment algorithm, frequent adjustment of a path model, use of a high-order adaptive linear transversal filter, instability of an adaptive linear recursive filter, failure to use a useful nonvibrational reference sensor, failure to deal with the nonlinear behavior of a primary or secondary path, weight adjustment using control predicted values, use of an identification neural network, and online adjustment of the weights of a neural network.read more
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References
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Journal ArticleDOI
Active control of vibration using a neural network
S.D. Snyder,N. Tanaka +1 more
TL;DR: It is concluded that more work is required to improve the predictability and consistency of the performance before the neural network controller becomes a practical alternative to the current linear feedforward systems.
Journal ArticleDOI
Improved training of neural networks for the nonlinear active control of sound and vibration
TL;DR: The results show that some of the new algorithms can greatly improve the learning rate of the neural-network control structure, and that for the considered experimental setup a neural- network controller can outperform linear controllers.
Patent
Neurocontrolled adaptive process control system
TL;DR: In this paper, the adaptive process control system selectively controls vibrations in a given medium in real-time, and the adaptive vibration cancellation provided by the invention takes place in real time, and without the need to process time-consuming complex mathematical algorithms.
PatentDOI
Active control system
Chinmoy Pal,Ichiro Hagiwara +1 more
TL;DR: In this paper, an active control system is provided with an actuator which controls a noise and/or vibration state of an automotive vehicle, a detector which detects the noise and or vibration states of the vehicle, and a control unit which receives an output signal of the noise detector and outputs a signal to control the actuator, the control unit having a neural net which compares a control predicted value based on the output signal with a control target value so that a correction for connection weights in the neural net is carried out on the basis of the comparison result.
Patent
Active attenuation of nonlinear sound
TL;DR: In this paper, a nonlinear transfer behavior between the control input 20 provided with the sensed input noise and the output 26 connected to the loudspeaker 12 has been proposed for attenuating undesirable high amplitude output sound of an acoustic system.
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