Optimization of Aeroengine Robust Controller Based on Adaptive Simulated Annealing Genetic Algorithm

Author(s):  
Wenxin Shao ◽  
Linfeng Gou ◽  
Xianyi Zeng ◽  
Yawen Shen ◽  
Jiang Yang
2011 ◽  
Vol 328-330 ◽  
pp. 1855-1859
Author(s):  
Tian Jian Sun ◽  
Jun Feng Shi ◽  
Xing He Yu

The development effect of steam flood well is influenced by the combination of the following parameters: injection speed, dryness fraction of steam, temperature of injection steam, and bottom hole flowing pressure. Taking the advantage of adaptive simulated annealing genetic algorithm with the characteristic of fast search and globally optimization, and combine with the mathematical model of the steam flood well. Maximization of the vapor-liquid interface factor is the target to optimize injection production parameter of the steam flood well, the results of the optimization shows that cumulative oil production increases obviously.


Author(s):  
Yunbo Bi ◽  
Weimiao Yan ◽  
Yinglin Ke

A large aircraft fuselage panel is commonly composed of a variety of thin-walled components. Most of these components are large, thin and compliant, and they are also prone to some flexible deformation during assembly and remain deformed after assembly. Besides, many different fabrication and assembly manners are adopted in order to guarantee the complicated assembly relationships between each component. The above characteristics often cause large aircraft fuselage panels to exhibit low stiffness and weak strength, thereby inducing deformation during assembly. Since the posture of a large aircraft fuselage panel is commonly evaluated by matching the theoretical and actual positions of the measurement points placed on it, and its assembly deformation is also represented by the position errors of the measurement points, a reasonable measurement point placement is significant for the large aircraft fuselage panel in digital assembly. This article presents a method based on the D-optimality method and the adaptive simulated annealing genetic algorithm to optimize the placement of the measurement points which can cover more deformation information of the panel for effective assembly error diagnosis. By taking the principle of the D-optimality method, an optimal set of measurement points is selected from a larger candidate set through adaptive simulated annealing genetic algorithm. As illustrated by an example, the final measurement point configuration is more effective to maximize the determinant of the corresponding Fisher Information Matrix and minimize the estimation error of the assembly deformation than those obtained by other methods.


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