Neural Networks for Smoothing of Helicopter Rotors

Abstract

A general, neural network based algorithm has been developed and applied to the problem of helicopter rotor smoothing. This approach provides non-parametric mappings between the spaces of rotor adjustment and vibration measurements, which are derived directly from empirical data, and permits to relax the usually used linearity assumption. Additionally, the rotor smoothing solutions are optimized to minimize not only the predicted vibration levels and track split but also the number of required adjustment moves. The neural network rotor smoothing system is a part of the VMEP (Vibration Management Enhancement Program) PC Ground Base Station program and has been successfully applied to the AH-64 Apache and UH-60 Blackhawk helicopters. Applications to other types of helicopters are under development.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 2001
Accession Number
ADA512076

Entities

People

  • Dariusz Wroblewski
  • Robert W. Branhof
  • Timothy Cook

Tags

Communities of Interest

  • Air Platforms
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Accuracy
  • Aircrafts
  • Algorithms
  • Computer Programming
  • Computers
  • Data Analysis
  • Detectors
  • Graphical User Interface
  • Ground Based
  • Ground Stations
  • Helicopter Rotors
  • Helicopters
  • Network Architecture
  • Neural Networks
  • Operating Systems
  • User Interface
  • Verification Tests

Fields of Study

  • Physics

Readers

  • Aerospace Engineering
  • Computer Vision.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks
  • Space