An improved PSO algorithm for parameter identification of nonlinear dynamic hysteretic models
An improved PSO algorithm for parameter identification of nonlinear dynamic hysteretic models
复制标题
非线性动态滞回模型参数辨识的改进PSO算法
DOI：
10.1016/j.jsv.2016.11.006
复制
发表时间：
2017-02-17
影响因子：
4.7
通讯作者：
Xia, Pinqi
中科院分区：
文献类型：
--
作者：
Zhang, Junhao;Xia, Pinqi
在非线性动力分析中所采用的非线性动力滞回模型一般包含大量的模型参数，需要准确有效地辨识。辨识的准确性和有效性通常取决于模型的复杂性、模型参数的个数以及参数初值的接近程度。粒子群优化算法（PSO）具有随机搜索能力，在非线性动力滞回模型的参数辨识中得到了广泛的应用。但粒子群算法容易陷入局部最优，出现早熟收敛，不能得到真实的最优解。通过定义滞回模型的适应度函数，提出了一种改进的粒子群算法，用于非线性动力滞回模型参数的辨识。改进的粒子群算法提高了算法的全局搜索能力，避免了传统粒子群算法的早熟收敛现象，并将其应用于旋翼桨叶的Leishman-Beddoes（LB）动态失速模型和作为旋翼超前-滞后阻尼器的弹性阻尼器的滞弹性位移场（ADF）模型的参数辨识。将辨识结果与试验结果进行比较，验证了改进粒子群算法对LB模型和ADF模型参数辨识的准确性和有效性。研究表明，为了减小粒子群算法的随机性对辨识参数精度的影响，增加重复辨识次数是一种有效的方法。(C)2016爱思唯尔有限公司版权所有
The nonlinear dynamic hysteretic models used in nonlinear dynamic analysis contain generally lots of model parameters which need to be identified accurately and effectively. The accuracy and effectiveness of identification depend generally on the complexity of model, number of model parameters and proximity of initial values of the parameters. The particle swarm optimization (PSO) algorithm has the random searching ability and has been widely applied to the parameter identification in the nonlinear dynamic hysteretic models. However, the PSO algorithm may get trapped in the local optimum and appear the premature convergence not to obtain the real optimum results. In this paper, an improved PSO algorithm for identifying parameters of nonlinear dynamic hysteretic models has been presented by defining a fitness function for hysteretic model. The improved PSO algorithm can enhance the global searching ability and avoid to appear the premature convergence of the conventional PSO algorithm, and has been applied to identify the parameters of two nonlinear dynamic hysteretic models which are the Leishman-Beddoes (LB) dynamic stall model of rotor blade and the anelastic displacement fields (ADF) model of elastomeric damper which can be used as the lead-lag damper in rotor. The accuracy and effectiveness of the improved PSO algorithm for identifying parameters of the LB model and the ADF model are validated by comparing the identified results with test results. The investigations have indicated that in order to reduce the influence of randomness caused by using the PSO algorithm on the accuracy of identified parameters, it is an effective method to increase the number of repeated identifications. (C) 2016 Elsevier Ltd. All rights reserved.