摘要
针对复合材料板簧的迟滞特性参数辨识工作,在梯度下降法的基础上提出了一种线性辨识方法,并推导得到递推公式,与已有工作中的改进萤火虫算法进行对比验证,从辨识精度和计算时间两个维度进行了讨论。在此基础上引入了动量项对线性辨识方法进行改进,相较改进前,算法在加速收敛时间的同时,降低了收敛误差。最后,将其用于某款复合材料板簧的迟滞特性辨识。结果表明:相对于智能启发式的非线性算法,线性辨识方法更适合进行复合材料板簧迟滞特性的辨识,其辨识精度与非线性算法相仿,且能将辨识计算时间控制在2.5 ms以内,使得在线参数辨识或数字孪生仿真等对实时性要求较高的工作成为可能。
Aiming at the identification of hysteresis characteristic parameter of composite leaf springs,a linear algorithm was proposed based on the stochastic gradient decent method and the recursive formular was deduced. From the two dimensions of identification accuracy and calculation time, the linear identification method and the improved firefly algorithm in the existing work are compared and verified. On this basis,a momentum term is introduced to improve the linear identification method. The results show that the improved algorithm accelerates the convergence time and reduces the convergence error. Finally,the improved algorithm was applicated to identify the hysteresis characteristics of a certain composite leaf spring and good results were obtained. The results show that the linear algorithm,which can get results in2.5 ms,is more suitable for hysteresis characteristic parameter identification of composite leaf springs than nonlinear algorithm and can be used in online parameter identification and digital twin simulation.
作者
李伟
宋海生
陆浩宇
史文库
王强
王晓俊
LI Wei;SONG Hai-sheng;LU Hao-yu;SHI Wen-ku;WANG Qiang;WANG Xiao-jun(School of Automotive Engineering,Shandong Jiaotong University,Jinan 250357,China;State Key Laboratory of Automotive Simulation and Control,Jilin University,Changchun 130022,China;School of Engineering and Machinery,Shandong Jiaotong University,Jinan 250357,China)
出处
《吉林大学学报（工学版）》
EI
CAS
CSCD
北大核心
2022年第4期829-836,共8页
Journal of Jilin University:Engineering and Technology Edition
基金
吉林省发改委项目(2019C041-4)
吉林省重点科技攻关计划项目(20170204063GX)。
关键词
车辆工程
复合材料
板簧
参数辨识
线性算法
在线辨识
vehicle engineering
composite material
leaf springs
parameter identification
linear algorithm
online parameter identification
