Constrained state–space system identification with application to structural dynamics

Paper in proceeding, 2006

Constrained identification of state-space models representing a structural dynamic systems is addressed. Based on physical insight, transfer function constraints are formulated in terms of the state-space parametrization. An example shows that a method tailored for this application, which utilizes the non-uniqueness of a state-space model, outperforms the classic sequential quadratic programming method in terms of robustness and convergence properties.