This position paper reflects on the state-of-the-art in decision-making under uncertainty. A
classical assumption is that probabilities can sufficiently capture all uncertainty in a system …

Controllers for dynamical systems that operate in safety-critical settings must account for
stochastic disturbances. Such disturbances are often modeled as process noise in a …

Capturing uncertainty in models of complex dynamical systems is crucial to designing safe
controllers. Stochastic noise causes aleatoric uncertainty, whereas imprecise knowledge of …

In the current control design of safety-critical cyber-physical systems, formal verification
techniques are typically applied after the controller is designed to evaluate whether the …

This paper presents a real-time trajectory planning framework for urban air mobility (UAM)
that is both safe and scalable. The proposed framework employs a decentralized, free-flight …

The advancement in communication, computation, and control technology has led to the
integration of the cyber-world and physical-world. This has also increased the incidence of …

Deep learning significantly impacts neural network controller synthesis. Despite the higher
efficiency of deep learning algorithms compared to traditional model-based controller design …

Tracking controllers are often integrated into control systems to ensure robustness against
uncertainties and disturbances during trajectory following maneuvers, where the design …

Reach-avoid analysis, which involves the computation of reach-avoid sets, is an established
tool that provides hard guarantees of safety (via avoiding unsafe states) and target …

Autonomous systems are supposed to automatically plan their actions and execute the plan
without human intervention. In this paper, we propose a model-based two-layer frame-work …