7 papers · 1 filter
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…
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…
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…
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.…
Learning State-Action Control Barrier Functions for Model-Free Control under Constraints
Kanghui He, Shengling Shi, Ton van den Boom +1
Ensuring constraint satisfaction for learning-based control is a critical challenge, especially in the model-free case. While safety filters address this challenge in the model-bas…
A state reduction approach for learning-based model predictive control for train rescheduling
Caio Fabio Oliveira da Silva, Xiaoyu Liu, Azita Dabiri +1
This paper proposes a state reduction method for learning-based model predictive control (MPC) for train rescheduling in urban rail transit systems. The state reduction integrates…
Probabilistically safe and efficient model-based reinforcement learning
Filippo Airaldi, Bart De Schutter, Azita Dabiri
This paper proposes tackling safety-critical stochastic Reinforcement Learning (RL) tasks with a sample-based, model-based approach. At the core of the method lies a Model Predicti…