Neural net controller for noise and vibration reduction
AssigneeGeneral Dynamics Advanced Technology Systems, Inc.
InventorsAntonios N. Kotoulas, Charles Berezin, Michael S. Torok, Peter F. Lorber
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AssigneeGeneral Dynamics Advanced Technology Systems, Inc.
InventorsAntonios N. Kotoulas, Charles Berezin, Michael S. Torok, Peter F. Lorber
Estimating the term…
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.
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:
The system according to 4 wherein said adaptive controller neural network receives said reference signal and at least one of said stimuli measurements as inputs and produces an adjustment signal.
A system for actively controlling the noise and vibration produced by the blades of a rotorcraft as measured by a plurality of noise and vibration sensors mounted on the rotor blades and within the rotorcraft by altering the aerodynamic characteristics of the rotor blades, comprising:
Dependencies are inferred from the claim text.