Nonparametric Continuous-Time Identification of Linear Systems: Theory, Implementation and Experimental Results

This paper presents an algorithm for continuous-time identification of linear dynamical systems using kernel methods. When the system is asymptotically stable, also the identified model is guaranteed to share such a property. The approach embeds the selection of the model complexity through optimization of the marginal likelihood of the data thanks to its Bayesian interpretation. The output of the algorithm is the continuous-time transfer function of the estimated model. In this work, we show the algorithmic and computational details of the approach, and test it on real experimental data from an Electro Hydro-Static Actuator (EHSA).