초록 ▼

Two neural networks are used to control adaptively a vibration and noise-producing plant. The first neural network, the emulator, models the complex, nonlinear output of the plant with respect to certain controls and stimuli applied to the plant. The second neural network, the controller, calculates

Two neural networks are used to control adaptively a vibration and noise-producing plant. The first neural network, the emulator, models the complex, nonlinear output of the plant with respect to certain controls and stimuli applied to the plant. The second neural network, the controller, calculates a control signal which affects the vibration and noise producing characteristics of the plant. By using the emulator model to calculate the nonlinear plant gradient, the controller matrix coefficients can be adapted by backpropagation of the plant gradient to produce a control signal which results in the minimum vibration and noise possible, given the current operating characteristics of the plant.

대표청구항 ▼

1. A system for actively controlling noise and vibration produced by the flow of a fluid over a surface as measured by a plurality of noise and vibration sensors by altering the flow of the fluid, said system comprising:a plant comprising at least a portion of a plurality of mechanical equipment inv

1. A system for actively controlling noise and vibration produced by the flow of a fluid over a surface as measured by a plurality of noise and vibration sensors by altering the flow of the fluid, said system comprising:a plant comprising at least a portion of a plurality of mechanical equipment involved in producing and measuring the noise and vibration; a preprocessing filtering module which isolates and quantifies the signals from the sensors at least one of which signals represent the measured noise and vibration desired to be controlled; means for measuring at least one stimulus related to a targeted noise and vibration; a reference signal; an adaptive controller neural network which receives an input from said reference signal and produces an adjustment signal; a means for altering the flow of the fluid by the state of said altering means and which receives as input said adjustment signal; an emulator neural network trained to model the dynamics of the fluid flow, said model receiving as input at least one of said stimulus measurements and the state of said altering means, calculating estimated noise and vibration signals, comparing the estimates with the results from the sensor preprocessing filtering module, producing a plant gradient signal representing an error in the model and feeding back said plant gradient signal into said controller neural network to adapt aid controller neural network; and a plurality of time-based filters which produces time-based representations of the inputs to said emulator neural network and said controller neural network.