Interval system identification for MIMO ARX models of minimal order

Stefan Zaiser, Michael Buchholz, Klaus Dietmayer

Abstract: Focus of this paper is on system identification of models in AutoRegressive with eXogenous inputs form from data with unknown, but bounded measurement errors. Hereby, these errors in data as well as the resulting uncertainty in parameters are represented by intervals. The proposed method can be applied to linear, time invariant systems with multiple inputs and multiple outputs. The main contribution are algorithms to determine the minimal order of a discrete-time model description in ARX form with interval parameters. In addition, an approach for using multiple sequences of measurement data is introduced. Finally, the method is demonstrated and discussed on a simulation example.