Constrained state–space system identification with application to structural dynamics

Constrained identification of state–space models representing structural dynamic systems is addressed. Based on physical insight, transfer function constraints are formulated in terms of the state–space parametrization. A simple 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. The method is also successfully applied to real experimental data of a plane frame structure.