电动负载模拟器摩擦模型参数辨识方法

Research on Parameter Identification Method of Friction Model for Electric Load Simulator

为实现电动负载模拟器的高精度控制,针对负载模拟器运行中受到的摩擦力影响问题,提出基于改进布谷鸟算法(improved cuckoo search algorithm,简称ICSA)的摩擦模型参数辨识方法。首先,搭建了电动负载模拟器试验样机,建立了动力学数学模型,并引入一种连续摩擦模型代替传统不连续摩擦模型;其次,将布谷鸟算法(cuckoo search algorithm,简称CSA)进行改进,在辨识中自动调整判定概率和步长的数值,提高了收敛速度和收敛精度;然后,通过逐点试验的方法得到了负载模拟器角速度范围为[-1,1]rad/s的摩擦力数据,并利用ICSA算法对摩擦力模型进行辨识;最后,进行了验证试验。试验结果表明:ICSA算法能准确快速地辨识出连续摩擦模型的6个参数,且收敛速度快、准确性高;当迭代达到最大迭代次数时,ICSA算法的目标函数值较CSA算法减小了45.2%。

light of operation affected by friction,parameter identification of a continuous differentiable friction model is introduced based on an improved cuckoo search algorithm(ICSA)to precisely control electric load simulators.First,the experimental prototype of an electric load simulator is set up,and the dynamic mathematical model is established in the way of a continuous friction model instead of the discontinuous common one.Second,the traditional CSA algorithm is improved by adjusting the values of the decision probability and the step size during the identification process,which accelerates the convergence speed and increases the accuracy.Then,the friction-velocity data with the speed range of[-1,1]rad/s is obtained by the point-by-point experiment method,and six parameters of the friction model are identified using the ICSA.Finally,verification experiments are conducted.The results show that the six parameters of the friction model are identified correctly and quickly by ICSA.The convergence speed and accuracy are improved compared with that of the traditional CSA,and the value of the cost function ICSA algorithm is reduced by 45.2%when the number of iterations reaches the maximum value.