Robust Safety Filters for Lipschitz-Bounded Adaptive Closed-Loop Systems with Structured Uncertainties

TL;DR

This paper introduces a novel adaptive safety framework for uncertain systems that ensures safety and stability during transient phases by leveraging Lipschitz bounds and convex optimization, improving over conservative existing methods.

Contribution

It develops a reference-based adaptive safety approach using barrier functions and Lipschitz bounds, reducing conservatism while guaranteeing safety and stability.

Findings

Reduces conservatism compared to traditional safety filters.

Guarantees forward invariance, stability, and tracking.

Reformulates safety conditions as a convex SOCP.

Abstract

Adaptive control provides closed-loop stability and reference tracking for uncertain dynamical systems through online parameter adaptation. These properties alone, however, do not ensure safety in the sense of forward invariance of state constraints, particularly during transient phases of adaptation. Control barrier function (CBF)-based safety filters have been proposed to address this limitation, but existing approaches often rely on conservative constraint tightening or static safety margins within quadratic program formulations. This paper proposes a reference-based adaptive safety framework for systems with structured parametric uncertainty that explicitly accounts for transient plant-reference mismatch. Safety is enforced at the reference level using a barrier-function-based filter, while adaptive control drives the plant to track the safety-certified reference. By exploiting Lipschitz bounds on the closed-loop error dynamics, a robust CBF condition is derived and reformulated as a convex second-order cone program (SOCP). The resulting approach reduces conservatism while preserving formal guarantees of forward invariance, stability, and tracking.