Model-Reference Compensator Synthesis for Uncertain Linear Systems Using Data-Driven Identified Plant Model

Abstract A model reference tracking controller that uses only input-output plant information is proposed for linear systems subjected to bounded unknown inputs. A design model for the compensator synthesis is obtained using a data-driven identification process. Using a data-driven identified plant model, an unknown input observer (UIO) is synthesized to estimate the state of the plant in the presence of uncertainties, which in this application are unknown inputs to the plant. A tracking controller is constructed to force the plant output to track a desired trajectory. A compensator is designed using linear matrix inequalities (LMI). The proposed compensator is tested on a vehicle model that performs a lane change maneuver. Bounds on the prescribed performance level for the state estimation and the tracking errors are given.