Control Theory for Multi-Agent Systems: Recent Advances and Applications

A Special Issue of Mathematics (ISSN 2227-7390) belonging to the section "E2: Control Theory and Mechanics".

Deadline for manuscript submissions: 31 March 2027 | Viewed by 1423

Editors


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Guest Editor
College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China
Interests: cooperative control; robust control; multi-agent systems; unmanned system swarms; reachable set estimation

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Guest Editor
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore City, Singapore
Interests: multi-agent systems; discrete-event system theory; model-based fault diagnosis; cyber security analysis and synthesis; control and optimization of complex networks with applications in flexible manufacturing; intelligent transportation; human–robot interface; power management and green buildings

Special Issue Information

Dear Colleagues,

In recent years, extensive research and irreplaceable applications of multi-agent systems (MASs) have emerged in the execution of complex tasks with high efficiency, enhanced robustness, and increased adaptability. With advances in engineering practices related to swarm intelligence, the coordinated control of MASs faces significant challenges in coping with communication constraints, dynamic uncertainties, and exogenous disturbances. This Special Issue aims to bridge the gap between theoretical breakthroughs in control synthesis for MASs and their engineering implementations in cooperative maneuvering for autonomous unmanned systems.

This Special Issue serves as a platform for researchers and engineers to share high-quality research on theoretical advances and practical applications of MASs. We are pleased to invite submissions of original research articles, reviews, and case studies in areas including, but not limited to, the following topics:

Theoretical advances:

Coordination regulation: Novel control strategies in consensus, containment, formation, and synchronization of MASs;

Distributed optimization: Distributed optimization algorithms and game-theoretic frameworks for networked systems;

Cooperative filtering: Advanced methodologies in cooperative information fusion and filtering over sensor networks;

Robust control: Significant control strategies in coping with dynamic uncertainties, exogenous disturbances, and parametric perturbations of MASs;

Networked control: Improved algorithms in event-triggered/sampled-data mechanisms and stability analysis under complex communication conditions.

Practical applications:

Unmanned aerial vehicles (UAVs): Swarm coordination, trajectory planning, and formation flying for drone clusters;

Unmanned surface vehicles (USVs): Cooperative motion control, formation maneuvering, and collision avoidance for marine vessels;

Unmanned underwater vehicles (UUVs): Cooperative navigation, target hunting, and formation maintenance in underwater environments;

Satellite systems: Collaborative Orbit control, attitude synchronization, and task planning of satellite systems;

Multi-robot systems: Cooperative manipulation, motion control, and logistics of multiple robots.

Dr. Xiangli Jiang
Prof. Dr. Rong Su
Guest Editors

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Keywords

  • multi-agent systems
  • coordination regulation
  • distributed optimization
  • cooperative filtering
  • robust control
  • networked control
  • autonomous unmanned systems
  • swarm intelligence

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Published Papers (1 paper)

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Research

28 pages, 3863 KB  
Article
DeepSORT-OCR: Design and Application Research of a Maritime Ship Target Tracking Algorithm Incorporating Hull Number Features
by Jing Ma, Xihang Su, Kehui Xu, Hongliang Yin, Zhihong Xiao, Jiale Wang and Peng Liu
Mathematics 2026, 14(6), 1062; https://doi.org/10.3390/math14061062 - 20 Mar 2026
Viewed by 664
Abstract
Maritime ship target tracking plays an important role in applications such as maritime patrol and maritime surveillance. However, complex sea conditions, similar target appearances, and long-distance imaging often lead to target identity confusion and unstable trajectories. To address these issues, in this paper, [...] Read more.
Maritime ship target tracking plays an important role in applications such as maritime patrol and maritime surveillance. However, complex sea conditions, similar target appearances, and long-distance imaging often lead to target identity confusion and unstable trajectories. To address these issues, in this paper, a ship multi-object tracking algorithm, DeepSORT-OCR, that integrates hull number semantic features is proposed. Based on the YOLO detection framework and the DeepSORT tracking architecture, a CBAM-ResNet network is introduced to enhance the representation of ship appearance features. An Inner-SIoU metric is adopted to improve the geometric matching of slender ship targets, while an LSTM-Adaptive Kalman Filter is employed to model the nonlinear motion patterns of ships and improve trajectory prediction stability. In addition, a Hull Number Feature Extraction module is designed in order to recognize ship hull numbers using OCR and match them with a hull number database. The extracted hull number semantic features are dynamically fused with visual appearance features to strengthen identity constraints during target association. The experimental results show that the proposed method achieves an MOTA of 66.53% on the MOT16 dataset, representing an improvement of 5.13% over DeepSORT. On the self-constructed maritime ship dataset, the method achieves an MOTA of 70.89% and an MOTP of 80.84%. Furthermore, on the hull-number subset, the MOTA further increases to 77.18%, an improvement of 7.31% compared with DeepSORT, while the number of ID switches is significantly reduced. In addition, experiments conducted on pure real data, pure synthetic data, and cross-domain evaluation settings demonstrate the stability and strong generalization capability of the proposed algorithm under different data distributions. The proposed method effectively improves the stability and identity consistency of ship multi-object tracking in complex maritime environments. Full article
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