Hybrid Evolutionary Optimisation Methods for the Clearance of Nonlinear Flight Control Laws

The application of two evolutionary optimisation 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 qualities 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 optimisation, are also developed and evaluated. Statistical comparisons of computational complexity and global convergence properties reveal the benefits of hybridisation for both algorithms. The differential evolution approach in particular, when appropriately augmented with local optimisation methods, is shown to have significant potential for improving both the reliability and efficiency of the current industrial flight clearance process.