Partitioning and distributed predictive control of UAV swarms

Distributed control; Drones; Model predictive control; Multi-agent systems; Swarm partition; Aerial vehicle; Complex task; Computational demands; Distributed predictive control; Distributed-control; High dimensionality; Model-predictive control; Multiagent systems (MASs); Real- time; Software; Control and Systems Engineering; Mathematics (all); Computer Science Applications

Abstract :

[en] Coordinating large swarms of unmanned aerial vehicles (UAVs) is a complex task due to high dimensionality and real-time computational demand. Effective formation control requires scalable strategies to maintain desired configurations while ensuring robustness to disturbances. This paper proposes a graph-based partitioning approach that divides the swarm into smaller, manageable subgroups, each of which is coordinated through an inter-partition communication network. This algorithm enables decentralized control, reducing computational complexity and enhancing scalability. This framework is integrated with a distributed control strategy, e.g., tube model predictive control, to ensure robust formation under uncertainties. Realistic simulations demonstrate the effectiveness of the proposed approach for UAV swarms.

Disciplines :

Electrical & electronics engineering

Author, co-author :

Delansnay, Gilles ; Université de Mons - UMONS > Faculté Polytechnique > Service Systèmes, Estimation, Commande et Optimisation

Ocampo-Martinez, Carlos ; Automatic Control Department (ESAII), Universitat Politècnica de Catalunya - BarcelonaTech, Avinguda Diagonal, 647, Barcelona, Spain

Dewasme, Laurent ; Université de Mons - UMONS > Faculté Polytechnique > Service Systèmes, Estimation, Commande et Optimisation

Vande Wouwer, Alain ; Université de Mons - UMONS > Faculté Polytechnique > Service Systèmes, Estimation, Commande et Optimisation

Language :

English

Title :

Partitioning and distributed predictive control of UAV swarms

Publication date :

August 2026

Journal title :

Robotics and Autonomous Systems

ISSN :

0921-8890

Publisher :

Elsevier B.V.

Volume :

202

Pages :

105473

Peer reviewed :

Peer Reviewed verified by ORBi

Additional URL :

https://api.elsevier.com/content/article/PII:S0921889026001466?httpAccept=text/xml

Research unit :

F107 - Systèmes, Estimation, Commande et Optimisation

Research institute :

R200 - Institut de Recherche en Energie

Available on ORBi UMONS :

since 05 June 2026

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