Neurocontrolled adaptive process control system

• US 5,426,720 A
• Filed: 09/17/1993
• Issued: 06/20/1995
• Est. Priority Date: 10/30/1990
• Status: Expired due to Term

• Current Assignee: George Washington University
• Original Assignee: Science Applications International Corporation
• Inventors: Bozich, Daniel J., MacKay, H. Bruce, Eggert, Jay A., Muenchau, Ernest E.
• Primary Examiner(s): MacDonald, Allen R.
• Assistant Examiner(s): DAVIS, GEORGE B
• Application Number:
  US08/123,642
• Time in Patent Office: 641 Days
• Field of Search: 395/22, 395/24, 395/903, 395/904, 395/906, 364/508, 318/114, 318/561
• US Class Current: 706/23
• CPC Class Codes: F16F 15/02 Suppression of vibrations of non-rotating, e.g. reciprocating systems; Suppression of vibrations of rotating systems by use of members not moving with the rotating systems F16F15/005 takes precedence ; layered products B32B; suppression of vibration in ships B63; relieving load on bearings, using magnetic means F16C39/06
• CPC Class Codes: G01N 29/30 Arrangements for calibrating or comparing, e.g. with standard objects
• CPC Class Codes: G05B 13/027 using neural networks only
• CPC Class Codes: G05D 19/02 characterised by the use of electric means
• CPC Class Codes: G10K 2210/1081 Earphones, e.g. for telephones, ear protectors or headsets
• CPC Class Codes: G10K 2210/12 Rooms, e.g. ANC inside a room, office, concert hall or automobile cabin
• CPC Class Codes: G10K 2210/1291 Anti-Vibration-Control, e.g. reducing vibrations in panels or beams
• CPC Class Codes: G10K 2210/3038 Neural networks
• CPC Class Codes: G10K 2210/3211 Active mounts for vibrating structures with means to actively suppress the vibration, e.g. for vehicles
• CPC Class Codes: G10K 2210/501 Acceleration, e.g. for accelerometers
• CPC Class Codes: G10K 2210/503 Diagnostics; Stability; Alarms; Failsafe
• CPC Class Codes: G21C 17/00 Monitoring; Testing measuring in general G01; Maintaining
• CPC Class Codes: H04S 3/00 Systems employing more than two channels, e.g. quadraphonic H04S5/00, H04S7/00 take precedence
• CPC Class Codes: Y10S 706/903 Control

Abstract

An adaptive process control system selectively controls vibrations in a given medium in real time. Unwanted vibrations present at a point being monitored in a given medium are sensed, and the system generates an appropriate offsetting vibration that is applied to the medium at a convenient location, which may be remote from the monitored point. The system includes a vibration sensor, such as one or more accelerometers, that sense both input and output vibrations present within the medium; at least one vibration generator, such as an electromagnetic shaker, that generates appropriate offsetting vibrations that are applied to the medium at one or more appropriate locations; and a neural network controller that controls the vibration generator(s) so as to force the sensed vibration at the monitored point(s) to a desired level. The adaptive vibration cancellation provided by the invention takes place in real time, and without the need to process time-consuming complex mathematical algorithms. A specific embodiment of the neural network controller includes a plurality of 4-layer neural networks configured in an adaptive filtered-x configuration.

Claims

1. Adaptive vibration control apparatus comprising:

(1) source sensing means for sensing source vibrations in a given medium;

(2) error sensing means for sensing vibrations at a specified monitoring point in the medium whereat vibrations are to be controlled;

(3) vibration generating means for generating at least one offsetting vibration that is applied to the medium at at least one location; and

(4) controller means responsive to the source vibrations sensed by the source sensing means and to the vibrations at the specified monitoring point sensed by the error sensing means for controlling the vibration generating means in real time so as to force the vibrations sensed by the error sensing means below a first level, said controller means comprising an artificial neural network, said artificial neural network having;

an input layer of a plurality of neurons to which time sequences of an input signal derived from said source sensing means is applied said input signals propagating forward through said network;

an output layer of at least one neuron providing an output signal for controlling said vibration generating means;

at least one intermediate layer of a plurality of neurons coupled to said input and output layers;

said input, output and at least one intermediate layers being responsive to at least one error signal derived from said error sensing means so as to adapt the amount of coupling that occurs between said input, intermediated said output layers as said input signal propagates therethrough.

2. The adaptive vibration control apparatus as set forth in claim 1 further including means for generating the derivative of said input signal and applying time sequences of said derivative to a portion of said input layer.

3. The adaptive vibration control apparatus as set forth in claim 2 wherein said artificial neural network further includes at least one set of a plurality of direct connections between said input layer and said output layer.

4. The adaptive vibration control apparatus as set forth in claim 3 wherein said artificial neural network includes two intermediate layers, each intermediate layer having at least eight neurons.

5. The adaptive vibration control apparatus as set forth in claim 4 wherein said first level below which said sensed vibration is forced by said controller means comprises a vibration level approximately equal to zero, whereby said vibration control apparatus effectively cancels the sensed vibration at the specified monitoring point.

6. The adaptive vibration control apparatus as set forth in claim 4 wherein said first level below which said sensed vibration is forced by said controller means comprises a time-varying reference value that changes as a function of time, whereby said vibration control apparatus functions as a vibration generator that generates a vibration that follows said time-varying reference value.

7. The adaptive vibration control apparatus as set forth in claim 1 wherein said controller means includes:

means for emulating said vibration generating means using a first artificial neural network;

a second artificial neural network coupled to said first artificial network, said second artificial network being adapted to control said vibrating generating means;

wherein each of said first and second artificial neural networks comprises;

an input layer of a plurality of neurons to which time sequences of an input signal is applied, said input signal propagating forward through said network,

an output layer of at least one neuron providing an output signal

at least one intermediate layer of a plurality of neurons coupled to said input and output layers;

wherein said input, output and at least one intermediate layers, are responsive to at least one error signal so as to adapt the amount of coupling that occurs between said input, intermediate, and output layers as said input signal propagates therethrough; and

further

wherein the input signal applied to the first artificial neural network is derived from the error sensing means, the input signal applied to the second artificial neural network is derived from the source sensing means, the output signal of the second artificial neural network drives said vibration generating means, the output signal from the first artificial neural network is compared with the output signal from the second artificial neural network to generate a first error signal that is applied to the first artificial neural network, and the input signal applied to the first artificial neural network is compared with the input signal applied to the second artificial neural network to generate a second error signal that is applied to the second artificial neural network.

8. The adaptive vibration control apparatus as set forth in claim 1 further including

a first artificial neural network (36x) that emulates said vibration generating means (34);

a second artificial neural network (32x) that controls said vibrating generating means;

a third artificial neural network (40x) coupled to said first artificial neural network; and

a fourth artificial neural network (38x) coupled to said second artificial neural network;

wherein each of said first, second, third and fourth artificial neural networks comprises;

an input layer of a plurality of neurons to which time sequences of an input signal is applied, said input signal propagating forward through said network,

an output layer of at least one neuron providing an output signal,

at least one intermediate layer of a plurality of neurons coupled to said input and output layers;

weighting means for defining the amount of coupling that occurs between said input, intermediate, and output layers as said input signal propagates therethrough; and

further

wherein the second artificial neural network receives its input signal from the source sensing means (A0), and directs its output signal to the vibration generating means, and

wherein the first artificial neural network receives its input signal from the output signal of the second artificial neural network and compares its output signal with an sensed error signal derived from said error sensing means (39) to produce a first error signal, said first error signal being applied to the weighting means of the first artificial neural network; and

wherein the third artificial neural network receives its input signal from the source sensing means, and directs its output signal to the fourth artificial neural network; and

wherein the fourth artificial neural network receives its input signal from the output signal of the third artificial neural network, and wherein a second error signal is applied to the weighting means of the fourth artificial neural network, the second error signal being derived from the source sensing means and error sensing means;

wherein the weighting means of the second artificial neural network track the weighting means of the fourth artificial neural network; and

the weighting means of the third artificial neural network track the weighting means of the first artificial neural network.