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Clearance of Nonlinear Flight Control Laws using Hybrid Evolutionary Optimization

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posted on 2007-11-19, 15:35 authored by P. P. Menon, Jongrae Kim, D. G. Bates, Ian Postlethwaite
The application of two evolutionary optimization methods, namely, differential evolution and genetic algorithms, to the clearance of nonlinear flight control laws for highly augmented aircraft is described. The algorithms are applied to the problem of evaluating a nonlinear handling quality clearance criterion for a simulation model of a high-performance aircraft with a delta canard configuration and a full-authority flight control law. Hybrid versions of both algorithms, incorporating local gradient-based optimization, are also developed and evaluated. Statistical comparisons of computational cost and global convergence properties reveal the benefits of hybridization for both algorithms. The differential evolution approach in particular, when appropriately augmented with local optimization methods, is shown to have significant potential for improving both the reliability and efficiency of the current industrial flight clearance process

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Citation

IEEE Transactions on Evolutionary Computation, 2006, 10 (6), pp.689-699

Published in

IEEE Transactions on Evolutionary Computation

issn

1089-778X

Copyright date

2006

Available date

2007-11-19

Publisher version

http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4016065

Language

en

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