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 …

In this article, we present a novel data-driven model predictive control (MPC) approach to
control unknown nonlinear systems using only measured input–output data with closed-loop …

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

Automated vehicles require efficient and safe planning to maneuver in uncertain
environments. Largely this uncertainty is caused by other traffic participants, eg, surrounding …

This paper presents a review of the design and application of model predictive control
strategies for Micro Aerial Vehicles and specifically multirotor configurations such as …

Guaranteed state estimation computes the set of possible states of dynamical systems given
the bounds of model uncertainties, disturbances, and noises. For the first time, we evaluate …

While reinforcement learning produces very promising results for many applications, its main
disadvantage is the lack of safety guarantees, which prevents its use in safety-critical …

Ensuring the safety of reinforcement learning (RL) algorithms is crucial to unlock their
potential for many real-world tasks. However, vanilla RL and most safe RL approaches do …

In this article, we develop a tracking model predictive control (MPC) scheme for nonlinear
systems using the linearized dynamics at the current state as a prediction model. Under …

Increasing penetration of renewable energy introduces significant uncertainty into power
systems. Traditional simulation‐based verification methods may not be applicable due to the …