Design of a Pilot-Activated Recovery System Using Genetic Search Methods

Abstract

Control design tasks often require the use of trial and error search methods to obtain a satisfactory solution. Depending upon the nature and the number of "tuning" parameters or functions, the search process can be very discontinuous and nonconvex. The genetic search methods are a recently developed family of techniques for optimization which offer certain advantages over other techniques. These include greater freedom in defining cost functions and constraints, and the ability to automate the design process. Most notably, though, is the ability to construct new control laws and the potential to generate non-intuitive solutions as well. This paper demonstrates the application of genetic search methods to design a Pilot-Activated Recovery System (PARS) for a modern fighter aircraft. The PARS is a guidance law that transfers the aircraft from any initial attitude to a wings level, nose-up, recovered flight condition. This system is useful in cases of pilot disorientation. A 6 degree-of freedom nonlinear model of a modern, high performance aircraft is used for design. The genetic search seeks to produce nonlinear feedback functions to meet the specified goals and constraints. This intricate problem highlights some of the advantages of this emerging search technique.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1999
Accession Number
ADA436379

Entities

People

  • G. D. Sweriduk
  • M. L. Steinberg
  • P. K. Menon

Tags

Communities of Interest

  • Air Platforms
  • Materials and Manufacturing Processes
  • Space

DTIC Thesaurus Topics

  • Aircrafts
  • Algorithms
  • Closed Loop Systems
  • Collision Avoidance
  • Computer Programming
  • Computer Programs
  • Control Systems
  • Control Systems Engineering
  • Dynamic Pressure
  • Fighter Aircraft
  • Flight Control Systems
  • Flight Paths
  • Genetic Algorithms
  • Guidance
  • Navigation
  • Recovery
  • Simulations

Readers

  • Aerodynamics/Aeronautics.
  • Neural Network Machine Learning.
  • Systems Analysis and Design

Technology Areas

  • Biotechnology