An improved method for dynamic load identification based on Tikhonov regularization

Dynamic load identification is an inverse problem that the dynamic loads are identified based on the unit impulse response and the dynamic load response. It involves the inverse of the matrix which is constructed with the unit impulse response. The matrix is a pathological matrix in most cases, so the inversed problem is generally ill-posed. At present, the Tikhonov regularization method is widely used in dynamic load identification. To improve the accuracy of the Tikhonov regularization method, this paper proposes an improved method. In this work, the dynamic load identification results with the matrixes and the dynamic load responses under different signal to noise (SNR) ratio are given to investigate the effects of the filter and the genetic algorithm (GA). The results indicate that the matrix filtered before the regularization and the GA adopted to obtain the regularization parameters can improve the accuracy of the result. There are three main processes in the improved method. Firstly, the matrix with disturbance are filtered to reduce the noise in them. Secondly, GA is used to obtain optimal regularization parameters. Finally, the loads are obtained by the Tikhonov regularization.