基于DKF和稀疏约束的激励和响应估计OA北大核心CSTPCD

To solve the problem of low frequency drift in excitation and response estimation by acceleration measurements,a method of excitation and response estimation based on DKF and sparse constraint is proposed.Firstly,the DKF algorithm is established based on a state-space model to separate the estimation of excitation and state.Secondly,considering the sparsity of excitation and the uncertainty of measurement noise,an inequality-constrained optimization model for excitation estimation is established based on CS.PM technique is used to solve the optimization problem so that the updated excitation is obtained.Finally,the modal superposition method is used to reconstruct various responses.The proposed method is verified by numerical simulation and test of a simply supported beam.The results show that,when acceleration sensors are collocated,the sparse solution of excitation can be obtained by the proposed method.By comparing the time history curve and the spectral diagrams of excitation and displacement,it is found that the low frequency components of the excitation and displacement are effectively suppressed with good robustness to noise,the method can still maintain the sparsity when two excitations are applied.When the acceleration sensors are non-collocated,the complete spatial sparse excitation cannot be estimated,but the unknown responses can still be estimated.