Accession Number:

ADA303704

Title:

Adaptive Optimization of Aircraft Engine Performance Using Neural Networks.

Descriptive Note:

Technical memo.,

Corporate Author:

NATIONAL AERONAUTICS AND SPACE ADMINISTRATION CLEVELAND OH LEWIS RESEARCH CEN TER

Personal Author(s):

Report Date:

1995-11-01

Pagination or Media Count:

15.0

Abstract:

Preliminary results are presented on the development of an adaptive neural network based control algorithm to enhance aircraft engine performance. This work builds upon a previous National Aeronautics and Space Administration NASA effort known as Performance Seeking Control PSC. PSC is an adaptive control algorithm which contains a model of the aircrafts propulsion system which is updated on-line to match the operation of the aircrafts actual propulsion system. Information from the on-line model is used to adapt the control system during flight to allow optimal operation of the aircrafts propulsion system inlet, engine, and nozzle to improve aircraft engine performance without compromising reliability or operability. Performance Seeking Control has been shown to yield reductions in fuel flow, increases in thrust, and reductions in engine fan turbine inlet temperature. The neural network based adaptive control, like PSC, will contain a model of the propulsion system which will be used to calculate optimal control commands on-line. Hopes are that it will be able to provide some additional benefits above and beyond those of PSC. The PSC algorithm is computationally intensive, it is valid only at near steady-state flight conditions, and it has no way to adapt or learn on-line. These increases are being addressed in the development of the optimal neural controller. Specialized neural network processing hardware is being developed to run the software, the algorithm will be valid at steady-state and transient conditions, and will take advantage of the on-line learning capability of neural networks. Future plans include testing the neural network software and hardware prototype against an aircraft engine simulation. In this paper the proposed neural network software and hardware is described and preliminary neural network training results are presented.

Subject Categories:

  • Cybernetics
  • Jet and Gas Turbine Engines

Distribution Statement:

APPROVED FOR PUBLIC RELEASE