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. 1. Introduction A validation and verification (clearance) process of flight control laws is required to prove and guarantee that the aircraft response is safe and stable for any possible failure case such as engine or actuator failures. In addition, the clearance process must take into account possible variations of flight parameters (e.g., large variations in mass, inertia, center of gravity positions, highly nonlinear aerodynamics, aerodynamic tolerances, and air data system tolerances). Also it is required to prove that pilot commands will not drive the aircraft response to critical operating points. It is noted that the aircraft flight quality and performance requirement are specified in the form of sets of stability performance and handling requirement criteria [3]. Consideration of all of these requirements makes the clearance process computationally complex, time consuming, and extremely expensive [2]. Therefore, the approach commonly used by investigator is to clear each performance/handling criterion individually. For a given performance/handling criterion, the clearance process requires finding for all possible configurations and for all combinations of parameter variations, and uncertainties, the worst-case scenario that violates the specified criterion in a specified flight envelope [3]. To date, very little research has been reported in the literature on the flight control law (FCL)
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