Data processing: artificial intelligence – Neural network – Learning task
Reexamination Certificate
2002-11-05
2004-06-15
Picard, Leo (Department: 2121)
Data processing: artificial intelligence
Neural network
Learning task
C700S030000, C700S048000, C700S280000
Reexamination Certificate
active
06751602
ABSTRACT:
FIELD OF THE INVENTION
The present invention relates to control systems for an active, adaptive vibration and noise attenuation system (AAVNAS). The present invention serves as the intelligence of an overall system that has several parts. Generally, the other parts of the noise control system are called the plant and would include the noise producing system itself, sensors for measuring the objectionable vibration and noise, a mechanism for altering the production of noise and vibration, and some parameter which can be measured independently of the noise and vibration which is related to the noise and vibration production and can serve as an element in the accurate estimation of noise and vibration. In particular, the present invention relates to a control system using neural networks to emulate and control the noise and vibration characteristics of a nonlinear plant.
BACKGROUND OF THE INVENTION
Virtually all dynamic mechanical devices produce vibration which, when transmitted through air within audible frequency ranges, is recognized as audible noise. Both the original vibration and the resultant audible noise have undesirable effects. Vibration in machinery can damage or degrade the machinery's performance. Noise and vibration perceived by people in the vicinity of the machinery may distract those people and cause fatigue or other physical maladies. Consequently, a need exists for systems and techniques which can be used to reduce noise and vibration.
Efforts to control noise and vibration can be classified into two categories: passive and active. Passive noise control techniques are distinguishable from active in that passive techniques are arranged to absorb energy from the plant or, by reason of tuned mounts, isolate the vibrating machinery and thus do not add any energy to the plant, i.e. the system being controlled. Many prior attempts to control noise and vibration utilized passive techniques such as mufflers or sound-absorbing insulation. However, passive noise and vibration control techniques approach practical limits in terms of cost and many other characteristics versus effectiveness. Further significant reductions in noise and vibration levels usually require advances in the state-of-the-art active control technology.
Active techniques seek to analyze the noise and vibration that the plant produces and then reduce the effects by either actively altering the characteristics of the system or by inducing acoustic wave interference accomplished by emitting noises/vibrations at specific time-delayed and phased-reversed frequencies in order to cancel out the noise and vibration from the plant. A more detailed explanation of the physics behind noise cancellation is given in an article entitled “A Primer on Active Sound and Vibration Control” written by Larry J. Eriksson which appeared on page 18 of the February, 1997, issue of “Sensors”.
One method known in the art is to measure the noise and vibration disturbances at locations where cancellation is desired and to feed this information back into an active controller which then makes alteration/cancellation adjustments to reduce the noise and vibration disturbances. Feedback systems tend to be effective when the time delay through the controller actuator and sensors is kept to a minimum.
Another method is to place a reference sensor as close as possible to the vibration
oise disturbance producing source in addition to the measurements of the noise and vibration disturbances at locations where cancellation is desired. Such a sensor is referred to as a reference sensor and allows the use of feed-forward algorithms. Feed-forward algorithms such as the Filtered-X Least Mean Squares (LMS) algorithm minimize the measured disturbance signals using a gradient descent algorithm to adapt the coefficient of a FIR (Finite Impulse Response) filter. With feed-forward systems, the FIR filter coefficients are updated so that the transfer function from the disturbance source to the disturbance signals where cancellation is desired, is equal to the net transfer function from the source through the reference sensor, FIR filter, and actuator to the same disturbance signals. The adaptive algorithm computes a FIR filter that best equalizes these two paths. U.S. Pat. No. 5,332,061 issued to Kamal Majeed, on Jul. 26, 1994, discloses one such system used to attenuate vibrations in a vehicle generated by an internal combustion engine. These algorithms are effective when the reference sensors are coherent with the error signals and have a small time delay with respect to the source and when the system controlled is linear. The Filtered-X LMS algorithm is described in the textbook “
Adaptive Signal Processing
by Bernard Widrow and Samuel Stearns © 1985, Prentice-Hall Inc., ISBN: 0-13-004029-0”.
Few practical implementations of nonlinear active controllers have been realized. Nonlinear active control systems are required when the actuators and/or the plant exhibit nonlinear dynamics. Neural networks are one method known in the art to model and control the behavior of nonlinear systems. A neural net plant emulator is first trained to identify the nonlinear plant behavior. Then, the neural net controller is trained in real time using the results of the emulator to control the actual noise and vibration disturbance signals. There are many publications on the training of neural nets such as backpropagation (with or without momentum), conjugate gradient, quasi-Newton algorithms and nonlinear Kalman Filtering. One such publication is
Neural Networks—A Comprehensive Foundation
by Simon Haykin, © 1994, Macmillan Publishing Company, ISBN: 0-02-352761-7.
U.S. Pat. No. 5,434,783, issued to Chinmoy Pal, on Jul. 18, 1995, discloses a system incorporating neural networks for use in canceling noise and vibration in an automobile. The Pal patent discloses a system using two neural networks. The “identification” neural net models the behavior of the plant being controlled. The “controller” net calculates the actuator command signals to reduce the automobile interior noise and vibrations of the vehicle body panel. The neural net architecture proposed in this patent includes feedback of the outputs from the neural net (controller or emulator) through ARMA (Auto-Regressive-Moving-Average) models to capture the temporal dynamic behavior of the plant. The output filtered signals from the ARMA models are then used as the inputs to the neural nets. In addition, feedback coupling exists from the emulator output, also filtered by alternate ARMA models to the controller inputs.
U.S. Pat. No. 5,386,689, issued to Daniel J. Bozich, on Feb. 7, 1995, discloses a system similar to Pal, utilizing dual neural networks to control actively vibration in gas turbine engines. This patent discloses the use of two neural nets, an emulator and a controller, to reduce the vibration and noise generated by a gas turbine engine, using actuators and sensors. The emulator in the Bozich patent is used to provide compensation to the neural net controller by using an idea similar to the Fx-LMS algorithm. A reference signal is passed in a feed-forward manner through the emulator to provide a filtered reference signal, which is then used to update the neural net controller weights. The Fx-LMS approach, and hence the approach of the Bozich patent, both assume that the plant (as represented by the emulator) and the controller are interchangeable and this in general is true for linear systems and may be possibly applicable for moderately linear systems.
The flow of a fluid over a surface is one situation in which noise and vibration can occur. Specific examples of this are the blades of a rotorcraft spinning through the air or the blades of a propeller or impeller spinning in water.
A substantial body of research into the noise and vibration generated by helicopter rotors exists. A helicopter emits a substantial amount of noise as it flies over an area. Noise and vibration levels within the helicopter cabin and throughout the airframe can also be significant. The external noise radiated from a h
Berezin Charles
Kotoulas Antonios N.
Lorber Peter F.
Torok Michael S.
General Dynamics Advanced Information Systems Inc.
Holmes Michael B.
Picard Leo
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