Table of Contents
ISRN Robotics
Volume 2013, Article ID 496457, 20 pages
Research Article

Flight Control Laws Verification Using Continuous Genetic Algorithms

1Department of Mechanical Engineering, The University of Jordan, P.O. Box 962069, Sport City, Amman 11196, Jordan
2Mechanical Engineering Department, King Faisal University, Al Hofuf, Saudi Arabia
3Department of Mechatronics Engineering, The University of Jordan, Amman, Jordan

Received 14 December 2012; Accepted 7 January 2013

Academic Editors: A. Hamzaoui and K. Watanabe

Copyright © 2013 A. Al-Asasfeh et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This work is concerned with the application of a continuous genetic algorithm (CGA) to solve the nonlinear optimization problem that results from the clearance process of nonlinear flight control laws. The CGA is used to generate a pilot command signal that governs the aircraft performance around certain points in the flight envelope about which the aircraft dynamics were trimmed. The performance of the aircraft model due to pitch and roll pilot commands is analyzed to find the worst combination that leads to a nonallowable load factor. The motivations for using the CGA to solve this type of optimization problem are due to the fact that the pilot command signals are smooth and correlated, which are difficult to generate using the conventional genetic algorithm (GA). Also the CGA has the advantage over the conventional GA method in being able to generate smooth solutions without the loss of significant information in the presence of a rate limiter in the controller design and the time delay in response to the actuators. Simulation results are presented which show superior convergence performance using the CGA compared with conventional genetic algorithms.