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A Robustness Analysis Tool for Fleetwide Variability and Degradation Assessment in Propulsion Control Systems

[+] Author Affiliations
Carl Byington, Michael Roemer, Sanket Amin

Impact Technologies, LLC, Rochester, NY

Pattada Kallappa

Pratt & Whitney, East Hartford, CT

Glen Karlsons

Naval Air Warfare Center, Patuxent River, MD

Paper No. GT2007-27666, pp. 687-695; 9 pages
doi:10.1115/GT2007-27666
From:
  • ASME Turbo Expo 2007: Power for Land, Sea, and Air
  • Volume 1: Turbo Expo 2007
  • Montreal, Canada, May 14–17, 2007
  • Conference Sponsors: International Gas Turbine Institute
  • ISBN: 0-7918-4790-X | eISBN: 0-7918-3796-3

abstract

The desire to enlarge the flight envelope of tactical aircraft and provide enhanced maneuvering capabilities has led to the use of forces and moments produced by the propulsion system to directly influence aircraft dynamics. This coupling between propulsion and flight dynamics is significant enough that traditional techniques for control system design and analysis are either conservative or inaccurate. An integrated approach is required in order to obtain an overall system that provides stability and performance with minimum pilot workload. The authors propose a solution that integrates existing techniques for linear robustness analysis, nonlinearity analysis, optimization, and robust identification into a software tool that facilitates the analysis of robustness of stability and dynamic performance of propulsion control systems and investigates the system’s ability to meet aggregate performance measures, specifically in the presence of fleet-wide component “variability.” The paper first discusses the motivation for the solution by identifying the variability problem in control system design. Following this, an overview of the proposed solution is presented with highlights of key elements, including a discussion on nonlinearity assessment techniques. The paper next describes the prototype version of the software tool and its initial analytical capabilities. Results of the prototype as applied against a nonlinear, engine control model using two different optimization routines, Genetic Algorithms and Particle Swarm Optimization, are presented to demonstrate the promising performance of both algorithms for finding the worst variability due to operating condition flight dynamics and aerothermal component degradation.

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