Constrained state–space system identification with application to structural dynamics
Artikel i vetenskaplig tidskrift, 2006
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.