collaborators

9 papers

eess.SY2026

Dynamics of Implicit Time-Invariant Max-Min-Plus-Scaling Discrete-Event Systems

Sreeshma Markkassery, Ton van den Boom, Bart De Schutter

Max-min-plus-scaling (MMPS) systems generalize max-plus, min-plus and max-min-plus models with more flexibility in modelling discrete-event dynamics. Especially, implicit MMPS mode…

eess.SY2026

Reinforcement Learning with Distributed MPC for Fuel-Efficient Platoon Control with Discrete Gear Transitions

Samuel Mallick, Gianpietro Battocletti, Dimitris Boskos +2

Cooperative control of groups of autonomous vehicles (AVs), i.e., platoons, is a promising direction to improving the efficiency of autonomous transportation systems. In this conte…

cs.RO2025

Model Predictive Control for Cooperative Docking Between Autonomous Surface Vehicles with Disturbance Rejection

Gianpietro Battocletti, Dimitris Boskos, Bart De Schutter

Uncrewed Surface Vehicles (USVs) are a popular and efficient type of marine craft that find application in a large number of water-based tasks. When multiple USVs operate in the sa…

cs.RO2025

Efficient Computation of a Continuous Topological Model of the Configuration Space of Tethered Mobile Robots

Gianpietro Battocletti, Dimitris Boskos, Bart De Schutter

Despite the attention that the problem of path planning for tethered robots has garnered in the past few decades, the approaches proposed to solve it typically rely on a discrete r…

eess.SY2025

Partitioning techniques for non-centralized predictive control: A systematic review and novel theoretical insights

Alessandro Riccardi, Luca Laurenti, Bart De Schutter

The partitioning problem is of central relevance for designing and implementing non-centralized Model Predictive Control (MPC) strategies for large-scale systems. These control app…

math.OC2025

Uncertainty Partitioning with Probabilistic Feasibility and Performance Guarantees for Chance-Constrained Optimization

Francesco Cordiano, Matin Jafarian, Bart De Schutter

We propose a novel distribution-free scheme to solve optimization problems where the goal is to minimize the expected value of a cost function subject to probabilistic constraints.…