  Implementation of learning-based control remains challenging due to the
absence of safety guarantees. Safe control methods have turned to model-based
safety filters to address these challenges, but this is paradoxical when the
ultimate goal is a model-free, data-driven control solution. Addressing the
core question of "Can we ensure the safety of any learning-based algorithm
without explicit prediction models and state estimation?" this paper proposes a
Data-Driven Safety Filter (DDSF) grounded in Behavioral System Theory (BST).
The proposed method needs only a single system trajectory available in an
offline dataset to modify unsafe learning inputs to safe inputs. This
contribution addresses safe control in the input-output framework and therefore
does not require full state measurements or explicit state estimation. Since no
explicit model is required, the proposed safe control solution is not affected
by unmodeled dynamics and unstructured uncertainty and can provide a safe
solution for systems with unknown time delays. The effectiveness of the
proposed DDSF is illustrated in simulation for a high-order
six-degree-of-freedom aerial robot and a time-delay adaptive cruise control
system.
