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Sampling-based stochastic data-driven predictive control under data uncertainty
J Teutsch, S Kerz, D Wollherr, M Leibold - ar** large commercial electric vehicle (EV) parking lots to support the rapid EV
adoption arouses interest in optimizing their real-time charging schedules with enhanced …
adoption arouses interest in optimizing their real-time charging schedules with enhanced …
A chance‐constrained tube‐based model predictive control for tracking linear systems using data‐driven uncertainty sets
This article presents a chance‐constrained tube‐based model predictive control (MPC)
method for tracking linear time‐invariant systems based on data‐driven uncertainty sets. By …
method for tracking linear time‐invariant systems based on data‐driven uncertainty sets. By …
Probabilistic safety regions via finite families of scalable classifiers
Supervised classification recognizes patterns in the data to separate classes of behaviours.
Canonical solutions contain misclassification errors that are intrinsic to the numerical …
Canonical solutions contain misclassification errors that are intrinsic to the numerical …
[HTML][HTML] A priori data-driven robustness guarantees on strategic deviations from generalised Nash equilibria
In this paper we focus on noncooperative games with uncertain constraints coupling the
agents' decisions. We consider a setting where bounded deviations of agents' decisions …
agents' decisions. We consider a setting where bounded deviations of agents' decisions …
Ensuring Safe Social Navigation via Explainable Probabilistic and Conformal Safety Regions
The recent advancements of Artificial Intelligence (AI) have generated a lot of interest in the
robotics community. Indeed, AI can find application in a wide variety of problems. Among …
robotics community. Indeed, AI can find application in a wide variety of problems. Among …
Adaptive stochastic predictive control from noisy data: A sampling-based approach
In this work, an adaptive predictive control scheme for linear systems with unknown
parameters and bounded additive disturbances is proposed. In contrast to related adaptive …
parameters and bounded additive disturbances is proposed. In contrast to related adaptive …
[PDF][PDF] A probabilistic scaling approach to conformal predictions in binary image classification
Deep learning solutions for image classification are more and more widespread and
sophisticated today, bringing the necessity to properly address their reliability. Many …
sophisticated today, bringing the necessity to properly address their reliability. Many …
[HTML][HTML] Prediction regions based on dissimilarity functions
This paper presents a new methodology to obtain prediction regions of the output of a
dynamical system. The proposed approach uses stored past outputs of the system and it is …
dynamical system. The proposed approach uses stored past outputs of the system and it is …
Nash equilibrium seeking for a class of quadratic-bilinear Wasserstein distributionally robust games
We consider a class of Wasserstein distributionally robust Nash equilibrium problems,
where agents construct heterogeneous data-driven Wasserstein ambiguity sets using …
where agents construct heterogeneous data-driven Wasserstein ambiguity sets using …