MIMO System Identification by Randomized Active-Set Methods

Jared Miller, Rajiv Singh, Mario Sznaier

Abstract: MIMO (multi-input multi-output) system identification is a particular instance of a parsimonious model selection problem. If the observed data is assumed to arise from a stable and low order plant, then the representing model should also be stable and have few poles in its realization. These constraints are challenging to impose in an L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> or nuclear norm framework, especially when observations are non-uniformly sampled. This paper implements MIMO identification by randomized active-set methods, as realized by Fully Corrective Frank-Wolfe (FCFW). Reweighting pole-group penalties allow for further system sparsification while monotonically decreasing the regularized fitting error. Efficacy of the approach is shown on two examples.