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Abstract
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.