A Novel Approach Utilizing Improved Genetic Algorithm for Parameter Optimization of Compressor Guide Vane Regulator
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.
compressor guide vane regulator fuzzy control genetic algorithm hybrid optimization algorithm parameter optimization AMESim iSIGHT Matlab/Simulink
YANG Fan FAN Ding PENG Kai
School of Power and Energy, Northwestern Ploytechnical University, Xian, China
国际会议
4th International Symposium on Jet Propulsion and Power Engineering(第四届喷气推进与动力工程国际会议 ISJPPE2012)
西安
英文
615-619
2012-09-10（万方平台首次上网日期，不代表论文的发表时间）