29 citations · 120 across the 27 of their papers we have counts for
4 papers · 1 filter
Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems
Truong X. Nghiem, Ján Drgoňa, Colin Jones +10
Physics-informed machine learning (PIML) is a set of methods and tools that systematically integrate machine learning (ML) algorithms with physical constraints and abstract mathema…
Model Predictive Control for Multi-Agent Systems under Limited Communication and Time-Varying Network Topology
Danilo Saccani, Lorenzo Fagiano, Melanie N. Zeilinger +1
In control system networks, reconfiguration of the controller when agents are leaving or joining the network is still an open challenge, in particular when operation constraints th…
Multi-agent Distributed Model Predictive Control with Connectivity Constraint
Andrea Carron, Danilo Saccani, Lorenzo Fagiano +1
In cooperative multi-agent robotic systems, coordination is necessary in order to complete a given task. Important examples include search and rescue, operations in hazardous envir…
Active Learning-based Model Predictive Coverage Control
Rahel Rickenbach, Johannes Köhler, Anna Scampicchio +2
The problem of coverage control, i.e., of coordinating multiple agents to optimally cover an area, arises in various applications. However, coverage applications face two major cha…