Input-Output Data-Driven Stabilization of Continuous-Time Linear MIMO Systems
By: Haihui Gao , Alessandro Bosso , Lei Wang and more
Potential Business Impact:
Keeps wobbly machines steady using past actions.
In this paper, we address the problem of data-driven stabilization of continuous-time multi-input multi-output (MIMO) linear time-invariant systems using the input-output data collected from an experiment. Building on recent results for data-driven output-feedback control based on non-minimal realizations, we propose an approach that can be applied to a broad class of continuous-time MIMO systems without requiring a uniform observability index. The key idea is to show that Kreisselmeier's adaptive filter can be interpreted as an observer of a stabilizable non-minimal realization of the plant. Then, by postprocessing the input-output data with such a filter, we derive a linear matrix inequality that yields the feedback gain of a dynamic output-feedback stabilizer.
Similar Papers
Data-Driven Control of Continuous-Time LTI Systems via Non-Minimal Realizations
Systems and Control
Teaches machines to control things with just their senses.
Data-Driven Stabilization of Unknown Linear-Threshold Network Dynamics
Systems and Control
Helps control brain signals to keep them steady.
Data-driven Internal Model Control for Output Regulation
Systems and Control
Makes robots learn to do jobs without knowing how.