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基于改進蟻群算法的風洞試驗俯仰機構(gòu)運動誤差優(yōu)化
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國家自然科學基金項目(U1530138)


Error Optimization of Pitching Mechanism Motion in Wind Tunnel Test Based on Improved Ant Colony Algorithm
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    摘要:

    為提高風洞試驗俯仰機構(gòu)的運動精度,減少初始誤差,提出了基于改進蟻群算法的俯仰機構(gòu)運動誤差優(yōu)化分析方法。針對影響俯仰機構(gòu)運動精度的3個誤差源——弧形導(dǎo)軌半徑R、連桿長度L、直線導(dǎo)軌安裝位置yOa,建立俯仰機構(gòu)運動誤差分析數(shù)學模型;推導(dǎo)了可用于分析誤差的改進蟻群算法模型,將俯仰機構(gòu)3個誤差源的求解轉(zhuǎn)換為對目標函數(shù)優(yōu)化問題的求解,采用改進算法進行誤差優(yōu)化。對比傳統(tǒng)數(shù)值方法,改進后的蟻群算法對誤差求解精度達到10-5mm級,有效地避免了結(jié)構(gòu)自身產(chǎn)生的初始誤差源對計算結(jié)果的影響。

    Abstract:

    In order to enhance the motion precision and reduce the initial error of the pitching mechanism which is used in the wind tunnel experiment, a novel error optimizing method was proposed. This new error optimizing approach is based on an improved ant colony algorithm. Firstly, three independent error sources which have influences on the motion precision of pitching mechanism were found and the mathematical models of three error sources including the radius of arc guide rail R, the length of connecting rod L and the installation position of linear guide rail yOa were established, respectively. The effect of each error source with the method of controlling variables was analyzed. Secondly, according to mathematical models of each error source, the mathematical model of three combined errors in the pitching mechanism was established. Based on the derived ant colony algorithm which is used in the error analyzing, the error optimizing problem could be converted to an optimizing problem of multiplied objectives. Finally, compared with results which utilize traditional Newton-Raphson iterative method, the motion accuracy of improved ant colony algorithm was higher, and the accuracy can reach a level of 10-5 mm. The compared results could also prove that the improved algorithm has a better global optimizing ability and it could avoid undesired effects of initial error in the structures when adopting the improved algorithm. The correctness and effectiveness of this method were confirmed by simulation with Matlab. In conclusion, the proposed approach was certificated to be effective and applicable in the engineering field.

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郭宗環(huán),謝志江,宋代平,齊 凱.基于改進蟻群算法的風洞試驗俯仰機構(gòu)運動誤差優(yōu)化[J].農(nóng)業(yè)機械學報,2016,47(7):375-381. Guo Zonghuan, Xie Zhijiang, Song Daiping, Qi Kai. Error Optimization of Pitching Mechanism Motion in Wind Tunnel Test Based on Improved Ant Colony Algorithm[J]. Transactions of the Chinese Society for Agricultural Machinery,2016,47(7):375-381.

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  • 收稿日期:2016-04-07
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  • 在線發(fā)布日期: 2016-07-10
  • 出版日期: 2016-07-10