Application of conjugate gradient least squares iteration regularization algorithm in impact load identification

Regularization methods should be developed to overcome the ill-posedness of inverse problem of structural dynamic load identification for getting a stable solution. The conjugate gradient least squares (CGLS) iterative regularization algorithm has several advantages over direct regularization methods such as the Tikhonov method on solving inverse problems: the inversion of matrix is not required, and no explicit regularization parameter is required. A CGLS iteration regularization algorithm with the heuristic stopping rule was proposed and as examples was applied to reconstruct the impact load acting on a three-degree-of-freedom system and a shell structure. The results were compared with those by the classical Landweber iteration regularization algorithm and Tikhonov regularization method. Simulations and experiments demonstrate that the CGLS algorithm for impact load identification works better in the aspects of accuracy, convergence rate, cost time and anti-noise.