【摘要】 本文基于显式Newmark-β法建立了适用于连续系统的动载荷时域识别方法，突破了传统Newmark-β法只适用于离散系统的局限。针对Newmark-β法在动载荷识别问题中出现的不适定性，本文提出了一种混合LSQR正则化修正方法。该方法在传统LSQR算法中融入了Tikhonov正则化思想，抑制求解过程中的数值震荡，引入一种基于响应残差的动态修正机制，通过对比计算响应与实测响应之间的残差，对载荷估计进行修正，该方法将Tikhonov正则化的稳定优势与迭代算法的适应性有效结合，显著改善了动载荷识别过程中的不适定性。通过典型工况的数值仿真，在不同噪声水平与多种载荷条件下验证了所提方法的鲁棒性与识别精度，性能明显优于传统正则化算法。随机载荷识别试验的研究结果表明，本文所提的混合LSQR正则化修正方法具备较强的抗噪能力与优异的识别精度。更多还原

【Abstract】 This study presents a time-domain identification method for dynamic loads in continuous systems,overcoming the limitation of the conventional Newmark-β method that is restricted to discrete systems. The proposed approach employs an explicit Newmark-β formulation and introduces a hybrid LSQR regularization correction technique to address the inherent ill-posedness in dynamic load identification. The novel method incorporates the idea of Tikhonov regularization into the standard LSQR algorithm to suppress numerical oscillations during the solution process. It introduces a dynamic correction mechanism based on response residuals, which adjusts the load estimation by comparing the residuals between the computed response and the measured response. By effectively combining the stability advantages of Tikhonov regularization with the adaptability of iterative algorithms,the method significantly improves the ill-posedness in the process of dynamic load identification. Numerical simulations of typical cases,with different noise levels and various load conditions,validate the robustness and identification accuracy of the proposed method. Experimental results from random load identification demonstrate that the proposed hybrid LSQR regularization correction method possesses strong noise resistance and excellent identification accuracy.更多还原