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Title: Clearance of Nonlinear Flight Control Laws using Hybrid Evolutionary Optimization
Authors: Menon, P. P.
Kim, Jongrae
Bates, D. G.
Postlethwaite, Ian
First Published: 2006
Citation: IEEE Transactions on Evolutionary Computation, 2006, 10 (6), pp.689-699
Abstract: 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
DOI Link: 10.1109/TEVC.2006.873220
ISSN: 1089-778X
Type: Article
Rights: Copyright © 2006 IEEE. Reprinted from IEEE Transactions on Evolutionary Computation, 2006, 10 (6), pp.689-699. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Leicester’s products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
Appears in Collections:Published Articles, Dept. of Engineering

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