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基于粒子群算法的農(nóng)用輪胎柔性環(huán)模型參數(shù)辨識(shí)方法
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國(guó)家重點(diǎn)研發(fā)計(jì)劃青年科學(xué)家項(xiàng)目(2022YFD2000300),、國(guó)家自然科學(xué)基金面上項(xiàng)目(52175259)和拼多多-中國(guó)農(nóng)業(yè)大學(xué)研究基金項(xiàng)目(PC2023B01005)


Parameter Identification Method for Agricultural Tire Flexible Ring Model Based on Particle Swarm Optimization Algorithm
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    摘要:

    輪胎柔性環(huán)模型能準(zhǔn)確表達(dá)輪胎變形,但模型的剛度參數(shù)無(wú)法直接測(cè)定,,因此模型剛度參數(shù)的辨識(shí)成為建模過程中的關(guān)鍵,。本文基于輪胎柔性環(huán)模型運(yùn)動(dòng)學(xué)方程,分析農(nóng)用輪胎固有頻率與剛度參數(shù)之間的關(guān)系,,提出基于粒子群算法的柔性環(huán)模型剛度參數(shù)辨識(shí)方法,。通過輪胎模態(tài)試驗(yàn)獲取輪胎固有頻率,采用粒子群算法對(duì)柔性環(huán)模型剛度參數(shù)進(jìn)行辨識(shí),。將固有頻率的試驗(yàn)值與預(yù)測(cè)值的平均誤差作為評(píng)價(jià)指標(biāo),,對(duì)比粒子群算法與傳統(tǒng)算法及遺傳算法辨識(shí)結(jié)果,結(jié)果表明粒子群算法的參數(shù)辨識(shí)結(jié)果精度較高,,平均絕對(duì)誤差為1.67Hz,,平均相對(duì)誤差為1.66%,相較于遺傳算法,,平均相對(duì)誤差降低16.16%,,運(yùn)算時(shí)間減少93.19%,。通過接地印痕試驗(yàn)獲取農(nóng)用輪胎接地角度,結(jié)合辨識(shí)所得剛度參數(shù),,估算輪胎所受到的垂向力,,對(duì)比垂向力的試驗(yàn)值與預(yù)測(cè)值,結(jié)果表明粒子群算法的參數(shù)辨識(shí)結(jié)果精度較高,,垂向載荷估算平均相對(duì)誤差為1.97%,,相對(duì)于遺傳算法,平均相對(duì)誤差降低12.05%,。

    Abstract:

    The tire flexible ring model can accurately express tire deformation, but the stiffness parameters of the model cannot be directly measured, so identifying the stiffness parameters of the model becomes the key in the modeling process. Based on the kinematic equation of the tire flexible ring model, the relationship between the natural frequency and stiffness parameters of agricultural tires was analyzed, and a method for identifying the stiffness parameters of the flexible ring model was proposed based on particle swarm optimization (PSO) algorithm. Based on the kinematics equation of the flexible ring model tire, the relationship between the natural frequency and the stiffness parameters of the agricultural tire was analyzed, and a method for identifying the stiffness parameter of agricultural tire flexible ring model based on PSO algorithm was proposed. A tire testing platform was built, the natural frequency was obtained through tire modal testing, and PSO algorithm was used to identify the stiffness parameters of the flexible ring model. Using the average error between the experimental and predicted values of the natural frequency as the evaluation index, the identification results of PSO algorithm were compared with traditional methods and genetic algorithm(GA). The results showed that PSO algorithm had the highest accuracy, with an average absolute error of 1.67Hz and an average relative error of 1.66%. Compared with GA, the average relative error was decreased by 16.16% and the computation time was decreased by 93.19%. The correctness and accuracy of the stiffness parameter identification method was proved based on PSO algorithm. The grounding angle of agricultural tires was obtained through the contact patch test, and the vertical force on the tires was estimated based on the identified stiffness parameters. The experimental and predicted values of vertical force were compared, and the results showed that the parameter identification results obtained by the particle swarm algorithm had the highest accuracy. The average relative error of vertical load estimation was 1.97%, which was reduced by 12.05% compared with the genetic algorithm.

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孫瑞,王亞東,李怡寧,何志祝,朱忠祥,李臻.基于粒子群算法的農(nóng)用輪胎柔性環(huán)模型參數(shù)辨識(shí)方法[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2024,55(4):402-410. SUN Rui, WANG Yadong, LI Yining, HE Zhizhu, ZHU Zhongxiang, LI Zhen. Parameter Identification Method for Agricultural Tire Flexible Ring Model Based on Particle Swarm Optimization Algorithm[J]. Transactions of the Chinese Society for Agricultural Machinery,2024,55(4):402-410.

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  • 收稿日期:2024-02-01
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  • 在線發(fā)布日期: 2024-04-10
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