Distributed Control

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12 papers4 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Distributed Performance Boosting through Neural Control for Plug-and-Play Networks
Oct 16, 2026Danilo Saccani, Leonardo Massai, Giancarlo Ferrari-TrecateDistributed Control Neural Network Stability
Ecole polytechnique federale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland.
Distributed Harmonic-Covariance Feedback for Hole Closure in Planar Robot Swarms
Oct 8, 2026Jitian Liu, Subhrajit BhattacharyaDistributed Control Swarm Robotics
Department of Mechanical Engineering and Mechanics, Lehigh University, Bethlehem, PA, USA.
Distributed Cascade Force Control of Soft-Tactile-Based Multi-robot System for Object Transportation
Oct 3, 2026Duy Anh Nguyen, Nhat Minh Dinh Le, Nhan Huu Nguyen +3Multi-Robot Cargo Transportation Distributed Control
More-Than-One Robotics Laboratory, Faculty of Sustainable Design Engineering, University of Prince Edward Island, Charlottetown, Canada · Soft Haptics Lab., School of Materials Science, Japan Advanced Institute of Science and Technology, Asahidai 1-1, Nomi, Ishikawa, 923-1292 Japan. · University of Engineering and Technology, Vietnam. +1
Action-Directed Information for Distributed Control and Agentic Interaction
Sep 23, 2026Shlomo DubnovDistributed Control Multi-Agent Communication
University of California San Diego La Jolla, USA
Distributed Model Predictive Control with Connectivity-based Contracts
Sep 17, 2026Jorit Geurts, Danilo Saccani, Melanie N. Zeilinger +1Multi-Robot Coordination Model Predictive Control
Institute for Dynamic Systems and Control, ETH Zurich, Switzerland · Institute of Mechanical Engineering, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
Distributed Non-Uniform Scaling Control of Multi-Agent Formation with Dynamic Agent Joining
May 28, 2026Tao He, Gangshan JingMulti-Robot Formation Control Distributed Control
School of Automation, Chongqing University, Chongqing, China
Preserving Topology Privacy of Network Systems by Feedback: Conditions and Distributed Design
May 15, 2026Yushan Li, Jiabao He, Julien M. Hendrickx +1Distributed Control
Department of Decision and Control Systems, KTH Royal Institute of Technology, Stockholm, Sweden · ICTEAM institute, UCLouvain, B-1348 Louvain-la-Neuve, Belgium
A Prototyping Framework for Distributed Control of Multi-Robot Systems
May 14, 2026Junaid Ahmed Memon, Allan Andre Do Nascimento, Kostas Margellos +1Multi-Robot Systems Robotics Simulation
Department of Engineering Science, University of Oxford, UK
Complex-Valued GNNs for Distributed Basis-Invariant Control of Planar Systems
Apr 3, 2026Samuel Honor, Mohamed Abdelnaby, Kevin LeahyDistributed Control Graph Neural Networks
Robotics Engineering Department, Worcester Polytechnic Institute, Worcester, MA 01609, USA
Robust Distributed Cooperative Path-Following and Local Replanning for Multi-UAVs Under Differentiated Low-Altitude Paths
Mar 25, 2026Zimao Sheng, Zirui Yu, Hong'an YangDistributed Control UAV Navigation
School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, Shaanxi, China · Tianjin University of Science and Technology, Tianjin, China
Engagement-Zone-Aware Input-Constrained Guidance for Safe Target Interception in Contested Environments
Mar 24, 2026Praveen Kumar Ranjan, Abhinav Sinha, Yongcan CaoDistributed Control Safety-Critical Control
Unmanned Systems Lab, Department of Electrical Engineering, The University of Texas at San Antonio, San Antonio, TX, 78249 · Guidance, Autonomy, Learning, and Control for Intelligent Systems (GALACxIS) Lab, Department of Aerospace Engineering and Engineering Mechanics, University of Cincinnati, OH 45221, USA
Douglas-Rachford Splitting for Group-Sparse Feedback Linear-Quadratic Control
Jul 26, 2025Lechen Feng, Xun Li, Yuan-Hua NiDistributed Optimization Distributed Control
Department of Applied Mathematics, The Hong Kong Polytechnic University, China · College of Artificial Intelligence, Nankai University, Tianjin, China

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Distributed Performance Boosting through Neural Control for Plug-and-Play Networks

Danilo Saccani, Leonardo Massai, Giancarlo Ferrari-Trecate

Ecole polytechnique federale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland.

Distributed Control Neural Network Stability Neural Operators

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We address the problem of improving the performance of pre-stabilized networked systems subject to plug-and-play (PnP) operations, namely additions or removals of subsystems. While distributed baseline controllers can preserve stability after a PnP transition, augmenting them with expressive neural policies can invalidate the formal stability guarantees. To overcome this issue, we build on recently proposed Performance Boosting controllers based on the Internal Model Control (IMC) principle, which reduce closed-loop stability to a finite

\ell_2

-gain requirement on an auxiliary operator. In the distributed setting, this operator is formed by interconnecting local neural operators that exchange information only between neighboring subsystems. We then derive free parametrizations of this interconnection, namely explicit maps from unconstrained trainable variables to interconnected neural operators satisfying a prescribed network gain bound for every parameter value. The obtained distributed neural controller can therefore be trained by unconstrained gradient-based optimization without stability constraints or projection steps, while retaining the prescribed gain bound at every training iterate. We develop two complementary parametrizations. A neighbor-based compositional parametrization uses local coupling bounds, so that after a PnP event only subsystems whose bounds change need to update their gain parameters. A network-norm parametrization instead uses a bound on the overall coupling matrix and can reduce conservatism, at the price of requiring network-wide information. A PnP DC-microgrid case study shows that the proposed neural layer improves voltage transients under topology changes and load variations while preserving nominal closed-loop stability.