Derivative free data-driven stabilization of continuous-time linear systems from input-output data

TL;DR

This paper introduces a derivative-free, data-driven method for stabilizing continuous-time linear systems using input-output data and filters, avoiding the need for derivative measurements.

Contribution

It proposes a novel framework that leverages filters and linear matrix inequalities to design stabilizing controllers directly from input-output data without derivatives.

Findings

Successfully stabilizes systems using only input-output data.

Avoids reliance on derivative measurements or estimates.

Employs linear matrix inequalities for controller synthesis.

Abstract

This letter presents a data-driven framework for the design of stabilizing controllers from input-output data in the continuous-time, linear, and time-invariant domain. Rather than relying on measurements or reliable estimates of input and output time derivatives, the proposed approach uses filters to derive a parameterization of the system dynamics. This parameterization is amenable to the application of linear matrix inequalities enabling the design of stabilizing output feedback controllers from input-output data and the knowledge of the order of the system.