A Novel Approach Utilizing Improved Genetic Algorithm for Parameter Optimization of Compressor Guide Vane Regulator

摘要

Aeroengine is an extremely complex and nonlinear system. Whereas there are many shortcomings in the conventional optimization methods, a novel improved genetic algorithm (fuzzy adaptive simulated annealing genetic algorithm with gradient direction, GFASAGA) will be proposed in this paper, which is used for parameter optimization for aeroengine compressor guide vane regulator (CGVR). Certain aeroengine CGVR was modeled on the basis of the co-simulation of AMESim and Simulink, the controller parameters of the regulator were optimized by GFASAGA in Matlab, standard genetic algorithm (SGA) and customized hybrid optimization algorithm in iSIGHT comparatively, then simulation results show that: the improved genetic algorithm is of good characteristics, such as global search, evolutionary rapidity and so on;the ultimate guide vane regulator formed by numerical simulation is provided with good static and dynamic characteristics, which has a great reference value for the engineer.