The Prediction of Pilot Opinion Ratings Using Optimal and Sub-Optimal Pilot Models

Abstract

This study details the development of a sub-optimal pilot model that blended the classical and optimal pilot model approaches in an attempt to achieve the advantages of each. This model used a numerical solution to the linear quadratic Gaussian problem to find the pilot gain, lead, and lag values that minimized a performance index consisting of task error and control usage. This development was conducted in four phases. First, an optimal pilot model developed by Systems Technology, Incorporated, was analyzed in detail. This analysis included a step-by-step example problem to clarify the model's logic and an in-depth sensitivity analysis of the model's parameters. Second, a ground and airborne evaluation of human pilot response was conducted using the Calspan variable stability Lear II aircraft. Primary pilot response parameters were recorded and examined using statistical and Fourier transform analysis in an attempt to provide insight into human pilot response. Third, a numerical solution to the linear quadratic Gaussian control problem that allows the compensator form to be predetermined was derived. Finally, the sub-optimal pilot model was developed and an analysis of the model's parameters was conducted.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1994
Accession Number
ADA278679

Entities

People

  • Craig R. Edkins

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Air Platforms
  • Cyber
  • Space

DTIC Thesaurus Topics

  • Air Force
  • Aircrafts
  • Algorithms
  • Computational Science
  • Control Systems
  • Data Science
  • Databases
  • Frequency
  • Frequency Response
  • Information Science
  • Mathematical Filters
  • Plastic Explosives
  • Regression Analysis
  • Standards
  • Statistical Algorithms
  • Statistical Analysis
  • Test And Evaluation

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Aviation Science / Aeronautics.
  • Computational Modeling and Simulation