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
Highlights
MODERN high-performance aircraft are often designed to be naturally unstable due to performance reasons and, can only be flown by means of a flight control system which provides artificial stability
This paper has compared the performance of two different evolutionary optimization algorithms, namely, genetic algorithms (GA) and differential evolution (DE), on a nonlinear flight control law clearance problem
Striking is the fact that DE achieves this improved accuracy in tracking the global solution with a reduced computational overhead—taking an average of 3086 simulations, 31% faster than the average of 4485 simulations required by GA
Summary
MODERN high-performance aircraft are often designed to be naturally unstable due to performance reasons and, can only be flown by means of a flight control system which provides artificial stability. The aircraft models used for clearance purposes describe the actual aircraft dynamics, but only within given uncertainty bounds One reason for this is the limited accuracy of the aerodynamic data set determined from theoretical calculations and wind tunnel tests. Faced with limited time and resources, the current flight clearance process employed by the European aerospace industry uses a gridding approach, whereby the various clearance criteria are evaluated for all combinations of the extreme points of the aircraft’s uncertain parameters [1]. This process is repeated over a gridding of the aircraft’s flight envelope.
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