Model Order Selection in Robust-Control-Relevant System Identification

Robust control allows for guaranteed performance for a range of candidate models. The aim of this paper is to investigate the role of model complexity in the identification of model sets for robust control. A key point is that model quality and model complexity should be evaluated with respect to the control goal. Regularization using a worst-case control criterion in conjunction with a specific model uncertainty structure allows robust control of multivariable systems using accurate models with low complexity. Simulations confirm that the model order should be selected in view of the control objectives. Overall, the framework allows for systematic identification of model sets for robust control. Copyright (C) 2021 The Authors.