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Nonlinear Model Predictive Control for Gas Turbine Engines

[+] Author Affiliations
Junxia Mu, David Rees

University of Glamorgan, Pontypridd, Wales, UK

Paper No. GT2004-53146, pp. 491-498; 8 pages
doi:10.1115/GT2004-53146
From:
  • ASME Turbo Expo 2004: Power for Land, Sea, and Air
  • Volume 2: Turbo Expo 2004
  • Vienna, Austria, June 14–17, 2004
  • Conference Sponsors: International Gas Turbine Institute
  • ISBN: 0-7918-4167-7 | eISBN: 0-7918-3739-4
  • Copyright © 2004 by ASME

abstract

In this paper Nonlinear Model Predictive Control (NMPC) is applied to a gas turbine engine. Since the performance of model based control schemes is highly dependent on the accuracy of the process model, the estimation of global nonlinear gas turbine models using NARMAX and neural network is first examined. To solve the NMPC problem, the Newton-based Levenberg-Marquardt Approach (NLMA) with hard constraints and Sequential Quadratic Programming (SQP) with soft constraints are validated using a wide range of large random, small and ramp signal tests. It is shown that the control performance using SQP is slightly better than that of NLMA, and proposed methods are robust in the face of large disturbances and model uncertainties. The results presented illustrate the improvement in the control performance using both methods over against gain-scheduling PID controllers.

Copyright © 2004 by ASME

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