Covariance-driven subspace identification: A complete input–output approach

摘要: Abstract In most of the available input–output covariance-driven subspace identification approaches knowledge input is exploited for eigenstructure only. authors' opinion a complete method should also cater estimation matrix B and direct feedthrough D state-space model. this paper, multivariate subspace-based formulation in time domain modal parameter using covariances developed. A novel covariance-based procedure estimating derived, with aim proposing applicable same way as its well-established data-driven counterpart. Detailed implementation issues are given validated through 15 dofs numerical example. As an advantage, when some user-defined parameters increased to obtain more accurate estimates or case very large data sets, not suffering from memory limitation problems that may affect method, due storing managing matrices. This demonstrated The tested on experimental application consisting thin-walled metallic structure: comparable computational effort, results similar those obtained by applying method.