On-line structure selection for multivariable state-space models

An algorithm is described for the selection of model structure for identifying state-space models of ‘black box’ character. The algorithm receives as ‘input’ a given system in a given parametrization. It is then tested whether this parametrization is suitable (well conditioned) for identification purposes. If not, a better one is selected and the transformation of the system to the new representation is performed. This algorithm can be used as a block both in an iterative, off-line identification procedure, and for recursive, on-line identification. It can be called whenever there is some indication that the model structure is ill-conditioned. It is discussed how the model structure selection algorithm can be interfaced with an off-line identification procedure. A complete procedure is described and tested on real and simulated data.