Controller Synthesis from Noisy-Input Noisy-Output Data
This addresses controller design for linear systems with measurement noise, but it appears incremental as it builds on existing data-driven control methods.
The paper tackles the problem of synthesizing dynamic output-feedback controllers for linear systems using noisy input-output data, and it results in a controller that robustly stabilizes all consistent systems, with extensions to multi-input multi-output systems demonstrated numerically.
We consider the problem of synthesizing a dynamic output-feedback controller for a linear system, using solely input-output data corrupted by measurement noise. To handle input-output data, an auxiliary representation of the original system is introduced. By exploiting the structure of the auxiliary system, we design a controller that robustly stabilizes all possible systems consistent with data. Notably, we also provide a novel solution to extend the results to generic multi-input multi-output systems. The findings are illustrated by numerical examples.