A derivative free framework for data-driven stabilization of continuous-time linear systems

This letter presents a novel data-driven framework for the design of control systems in the continuous-time domain. Differently from conventional methodologies that rely on measurements or estimates of the time derivatives of the plant state, the proposed approach utilizes filters of the input and the state of the plant to derive a parameterization for continuous-time linear time-invariant feedback systems. This parameterization is solely dependent on measurable quantities, enabling the direct application of linear matrix inequalities to solve stabilization problems. Furthermore, the framework explicitly addresses process and measurement noise, resulting in robust controller synthesis.