New methods for the identification of a stable subspace model for dynamical systems

Dynamical system identification is a widely studied problem. Among all the available models, linear ones are probably the most used, thanks to their efficiency and the theoretical comprehension they allow on the real system. Stability, which is an attractive characteristic of systems, can be simply expressed for the linear systems. As some identification algorithms do not guarantee the stability of the calculated model, this property is used to extend efficiently these procedures to stable system identification. The different approaches proposed in the literature are studied and some new algorithms are proposed to solve this problem. The algorithms are then compared on a simple example to measure their performances.