In this paper, a non-linear parameter identification method for structures is presented, whereby the instantaneous power flow balance of the substructure of interest is enforced. The time-domain power flow into the non-linear substructure is balanced against the power transmitted to adjacent structures, damping and kinetic and strain energies. Enforcing this condition of matching the net power balance to zero, a numerical model is iteratively updated as an inverse problem. Here, the identification is carried out using a non-classical optimization search tool, particle swarm optimization algorithm. A cubic non-linearity in spring and a quadratic non-linearity in damper are modelled for non-linear parameter estimation using power flow concept and is a preliminary stage of non-linear joint parameter identification. Important numerical simulations are presented in this paper, which cover different load cases, measurement points and different combination of non-linearities under noise-free and noisy conditions. The identified results show significant improvements in non-linear identification accuracy compared to previous literature related to non-linear identification.

本文提出了一种用于结构的非线性参数识别方法，从而实现了感兴趣的子结构的瞬时功率流平衡。流入非线性子结构的时域功率与传递到相邻结构的功率，阻尼以及动能和应变能之间是平衡的。强制将净功率平衡匹配为零的这种条件，将数值模型迭代更新为反问题。在此，使用非经典优化搜索工具（粒子群优化算法）进行识别。使用潮流概念对弹簧中的立方非线性和阻尼器中的二次非线性进行建模，以进行非线性参数估计，这是非线性关节参数识别的初步阶段。本文提出了重要的数值模拟，涵盖了在无噪声和高噪声条件下的不同载荷工况，测量点和非线性的不同组合。与先前有关非线性识别的文献相比，所识别的结果显示出非线性识别准确性的显着提高。