Research on Cooperative Control of Multi-agent Unmanned Systems

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".

Deadline for manuscript submissions: 15 June 2025 | Viewed by 1784

Special Issue Editors


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Guest Editor
Unmanned System Research Institute, Northwestern Polytechnical University, Xi’an 710072, China
Interests: cooperative gudance and control; nonlinear control; intelligent decision-making; machine learning in aerospace engineering
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E-Mail Website
Guest Editor
Unmanned System Research Institute, Northwestern Polytechnical University, Xi’an 710072, China
Interests: cooperative control; dynamics and control

Special Issue Information

Dear Colleagues,

Multi-agent unmanned systems have rapidly developed with the resurgence of artificial intelligence in the past decade. The use of multi-agent unmanned systems has grown explosively in both military areas (such as surveillance, reconnaissance, and attack) and civil areas (such as transportation, mapping, and rescue). The cooperative control of multi-agent unmanned systems is key to coordinating the actions of multiple autonomous agents to achieve shared goals. Certainly, research on the cooperative control of multi-agent unmanned systems has been being extremely active.

The goal of this Special Issue is to report the latest theoretical findings and innovative applications in the cooperative control of multi-agent unmanned systems, providing a platform for the community to quickly share new ideas and practical experiences. Thus, this Special Issue focuses upon research on theories, frameworks, methods, and applications of the cooperative control of multi-agent unmanned systems, ranging from unmanned underwater vehicles to planet rovers. This Special Issue particularly emphasizes the cooperative control of UUVs, USVs, UGVs, and UAVs; spacecraft formation; cooperative guidance; cooperative integrated pose control; and distributed optimization of multi-agent unmanned systems. This collection concentrates on multiple unmanned systems, excluding multi-agent systems such as smart grid systems, computer network systems, biological systems, etc., to better reveal the development of cooperative control in the field of unmanned systems.

Prof. Dr. Hanqiao Huang
Dr. Zhang Bo
Guest Editors

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Keywords

  • cooperative control of UUVs, USVs, UGVs, and UAVs
  • cooperative control of spacecraft formation
  • cooperative guidance
  • cooperative integrated pose control
  • distributed optimization of multi-agent unmanned systems

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Published Papers (2 papers)

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Research

16 pages, 878 KiB  
Article
Distributed Adaptive Formation Control for Second-Order Multi-Agent Systems Without Collisions
by Juan Francisco Flores-Resendiz, Jesus David Aviles-Velazquez, Claudia Marquez, Rigoberto Martinez-Clark and Maria Alejandra Rojas-Ruiz
Electronics 2025, 14(9), 1751; https://doi.org/10.3390/electronics14091751 - 25 Apr 2025
Viewed by 207
Abstract
This paper presents an adaptive strategy to solve the formation control problem for a set of second-order agents with parametric uncertainty and nonlinearity. The strategy regards a group of agents where the nonlinearities and uncertainties are represented by a linearly parametrized term, which [...] Read more.
This paper presents an adaptive strategy to solve the formation control problem for a set of second-order agents with parametric uncertainty and nonlinearity. The strategy regards a group of agents where the nonlinearities and uncertainties are represented by a linearly parametrized term, which allows us to consider non-identical agents. In order to ensure the collision-free motion of agents, we propose the use of a repulsive vector field component that is applied only when a pair of agents becomes nearer than a predefined minimum bound. Numerical simulations were carried out to show the effectiveness of the proposed scheme. First, a simplified example was used to verify the key features of the control law, followed by a general case to illustrate the performance of the algorithm in a more complex scenario. Full article
(This article belongs to the Special Issue Research on Cooperative Control of Multi-agent Unmanned Systems)
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14 pages, 4346 KiB  
Article
Robust Sparse Bayesian Learning Source Localization in an Uncertain Shallow-Water Waveguide
by Bing Zhang, Rui Jin, Longyu Jiang, Lei Yang and Tao Zhang
Electronics 2024, 13(23), 4789; https://doi.org/10.3390/electronics13234789 - 4 Dec 2024
Viewed by 697
Abstract
Conventional matched-field processing (MFP) for acoustic source localization is sensitive to environmental mismatches because it is based on the wave propagation model and environmental information that is uncertain in reality. In this paper, a mode-predictable sparse Bayesian learning (MPR-SBL) method is proposed to [...] Read more.
Conventional matched-field processing (MFP) for acoustic source localization is sensitive to environmental mismatches because it is based on the wave propagation model and environmental information that is uncertain in reality. In this paper, a mode-predictable sparse Bayesian learning (MPR-SBL) method is proposed to increase robustness in the presence of environmental uncertainty. The estimator maximizes the marginalized probability density function (PDF) of the received data at the sensors, utilizing the Bayesian rule and two hyperparameters (the source powers and the noise variance). The replica vectors in the estimator are reconstructed with the predictable modes from the decomposition of the pressure in the representation of the acoustic normal mode. The performance of this approach is evaluated and compared with the Bartlett processor and original sparse Bayesian learning, both in simulation and using the SWellEx-96 Event S5 dataset. The results illustrate that the proposed MPR-SBL method exhibits better performance in the two-source scenario, especially for the weaker source. Full article
(This article belongs to the Special Issue Research on Cooperative Control of Multi-agent Unmanned Systems)
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