In control theory, complicated dynamics such as systems of (nonlinear) differential equations
are controlled mostly to achieve stability. This fundamental property, which can be with …

This vision article shows how to build on the framework of event-triggered Control Barrier
Functions (CBFs) to design model-free controllers for safety-critical multi-agent systems with …

This article is concerned with a formal verification scheme for both discrete-and continuous-
time deterministic systems with unknown mathematical models. The main target is to verify …

This paper studies formal synthesis of controllers for continuous-space systems with
unknown dynamics to satisfy requirements expressed as linear temporal logic formulas …

In this work, we propose a data-driven approach to synthesize safety controllers for
continuous-time nonlinear polynomial-type systems with unknown dynamics. The proposed …

We provide a data-driven approach equipped with a formal guarantee for verifying the safety
of stochastic systems with unknown dynamics. First, using a notion of barrier certificates, the …

The desire to use reinforcement learning in safety-critical settings has inspired a recent
interest in formal methods for learning algorithms. Existing formal methods for learning and …

This letter proposes a data-driven framework to synthesize safety controllers for nonlinear
systems with finite input sets and unknown mathematical models. The proposed scheme …

This article addresses the problem of safety-critical control for multiagent systems with
unknown dynamics in unknown environments. It has been shown that stabilizing affine …

We introduce a novel approach for the construction of symbolic abstractions-simpler, finite-
state models-which mimic the behaviour of a system of interest, and are commonly utilized to …