Reachability analysis consists in computing the set of states that are reachable by a
dynamical system from all initial states and for all admissible inputs and parameters. It is a …

Deep reinforcement learning (DRL) has proven capable of superhuman performance on
many complex tasks. To achieve this success, DRL algorithms train a decision-making agent …

This paper provides an introduction and overview of recent work on control barrier functions
and their use to verify and enforce safety properties in the context of (optimization based) …

The proven efficacy of learning-based control schemes strongly motivates their application
to robotic systems operating in the physical world. However, guaranteeing correct operation …

In this article, a novel adaptive tracking control technique is developed for multiple-input-
multiple-output nonlinear systems with model uncertainty and under output constraints …

This paper extends control barrier functions (CBFs) to high order control barrier functions
(HOCBFs) that can be used for high relative degree constraints. The proposed HOCBFs are …

This paper develops a control methodology that unifies control barrier functions and control
Lyapunov functions through quadratic programs. The result is demonstrated on adaptive …

We consider the problem of generating motion plans for a robot that are guaranteed to
succeed despite uncertainty in the environment, parametric model uncertainty, and …

A foundational text that offers a rigorous introduction to the principles of design,
specification, modeling, and analysis of cyber-physical systems. A cyber-physical system …

Although there are fruitful results on adaptive control of constrained parametric/
nonparametric strict-feedback nonlinear systems, most of them are contingent upon …