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基于遗传算法的飞机俯仰控制参数优化设计及重构 被引量:3

Genetic Algorithm Based Aircraft Pitch Control Parameter Optimization Design and Reconfiguration
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摘要 遗传算法不受问题性质(如连续性,可微性)的限制,能够处理传统优化算法难以解决的复杂问题,故近年来在控制参数优化方面得到了广泛的应用;但算法中,交叉概率与变异概率的选择没有给出通用标准,通过多种交叉概率与变异概率组合的优化结果比较确定合适的交叉概率与变异概率,然后从几种控制系统常用性能指标中选择最合适的一种做为适应度函数,优化得无故障时控制参数,按照重构后系统开环传递函数保持不变,即系统闭环传函极点不发生变化的原则,由升降舵损伤程度和无损伤时控制参数可得重构后的控制参数;由仿真实验可得,此方法能够较快较精确地确定控制参数,系统无超调,控制效果良好,重构后,系统性能能够保持不变,达到良好的重构效果。 Genetic algorithms are not limited by the nature of the problems (such as continuity, differentiability), can handle complex problems that traditional optimization algorithms can' t solve. It has been widely applied in control parameters optimization in recent years. But there is not a common standard to choose its crossover probability and mutation probability, by means of the comparison between differ- ent groups of optimization results by different crossover probability and mutation probability to determine the appropriate crossover probabili- ty and mutation probability, and select the most suitable one from several control systems commonly used performance indicators as the fit- ness function, get the control parameters when there~ s no fault on the aircraft. When the system open--loop transfer function remains un changed, the system closed--loop transfer function poles does not change, by the elevator damage degree and control parameters when there's no damage control parameters after reconstructed can be obtained. The simulation result shows that the system using the method based on is genetic algorithms of no vershoot. As well, the settletime are shorter. The better control effect than the traditional design meth ods. after reconstruction, the system performance can be maintained constant, the method achieves a good effect on the reconstruction
出处 《计算机测量与控制》 北大核心 2014年第6期1780-1782,1789,共4页 Computer Measurement &Control
关键词 遗传算法 交叉概率 变异概率 适应度函数 升降舵 genetic algorithms crossover probability mutation probability fitness function elevator.
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