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Electronics
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Advanced Control Strategies and Applications of Multi-Agent Systems
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Advanced Control Strategies and Applications of Multi-Agent Systems

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

Deadline for manuscript submissions: 15 February 2025 | Viewed by 2765

Special Issue Editors

Dr. Han Gao

E-Mail Website
Guest Editor
School of Automation, Beijing Institute of Technology, Beijing 100081, China
Interests: nonlinear control; adaptive control; multi-agent systems; spacecraft formation control; aircraft formation control
Dr. Guangchen Zhang

E-Mail Website
Guest Editor
School of Mathematics and Information Science, North Minzu University, Yinchuan 750030, China
Interests: multidimensional systems; complex networks; sliding mode control; multi-agent systems
Prof. Dr. Xin Wang

E-Mail Website
Guest Editor
School of Mathematical Science, Heilongjiang University, Harbin 150080, China
Interests: adaptive control; fault detection and fault-tolerant control; multi-agent coordination; cyber–physical system
Dr. Jinwen Hu

E-Mail Website
Guest Editor
Key Laboratory of Information Fusion Technology, Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi’an 710072, China
Interests: unmanned systems; information fusion; distributed control; navigation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue (SI) aims to explore the latest advancements in multi-agent system (MAS) control strategies and practical applications. MASs, consisting of multiple interacting intelligent agents, have the potential to solve intricate problems through a form of coordination and distributed decision making that single agents could not handle, with significant implications for various domains such as robotics, autonomous vehicles, aircraft formation, spacecraft formation, smart grids, and distributed sensor networks. The coordination of MAS presents unique challenges and opportunities for enhancing system efficiency, resilience, and adaptability. One major challenge is designing robust and adaptive control algorithms that can handle MAS in dynamic and uncertain environments. Ensuring reliable and efficient communication among agents is another critical area. Additionally, a key research direction involves reducing communication resource usage and improving efficiency in the decision-making process.

This SI seeks high-quality submissions highlighting cutting-edge research and developments in MASs, focusing on their control, coordination, communication, and learning capabilities. The topics of interest include advanced control algorithms, cooperative behaviors, distributed decision making, adaptive learning mechanisms, and MAS applications. The goal is to provide a platform for researchers and practitioners to share their latest theoretical, experimental, and applied research findings, fostering a deeper understanding and broader application of MASs in solving real-world problems, pushing the boundaries of what is currently possible.

Dr. Han Gao
Dr. Guangchen Zhang
Prof. Dr. Xin Wang
Dr. Jinwen Hu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • multi-agent systems
  • formation control
  • consensus tracking
  • event-triggered control
  • dynamic network topology
  • distributed control
  • distributed optimization
  • resilient consensus
  • game theory
  • non-cooperative games
  • mixed-motive games
  • multi-agent reinforcement learning

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Published Papers (4 papers)

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Research

23 pages, 9179 KiB  
Article
Robust Twin Extreme Learning Machine Based on Soft Truncated Capped L1-Norm Loss Function
by Zhendong Xu, Bo Wei, Guolin Yu and Jun Ma
Electronics 2024, 13(22), 4533; https://doi.org/10.3390/electronics13224533 - 19 Nov 2024
Viewed by 365
Abstract
Currently, most researchers propose robust algorithms from different perspectives for overcoming the impact of outliers on a model, such as introducing loss functions. However, some loss functions often fail to achieve satisfactory results when the outliers are large. Therefore, the capped loss has [...] Read more.
Currently, most researchers propose robust algorithms from different perspectives for overcoming the impact of outliers on a model, such as introducing loss functions. However, some loss functions often fail to achieve satisfactory results when the outliers are large. Therefore, the capped loss has become a better choice for researchers. The majority of researchers directly set an upper bound on the loss function, which reduces the impact of large outliers, but also introduces non-differentiable regions. To avoid this shortcoming, we propose a robust twin extreme learning machine based on a soft-capped L1-normal loss function (SCTELM). It uses a soft capped L1-norm loss function. This not only overcomes the shortcomings of the hard capped loss function, but also improves the robustness of the model. Simultaneously, to improve the learning efficiency of the model, the stochastic variance-reduced gradient (SVRG) optimization algorithm is used. Experimental results on several datasets show that the proposed algorithm can compete with state-of-the-art algorithms in terms of robustness. Full article
(This article belongs to the Special Issue Advanced Control Strategies and Applications of Multi-Agent Systems)
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16 pages, 3001 KiB  
Article
An Explainable Data-Driven Optimization Method for Unmanned Autonomous System Performance Assessment
by Hang Yi, Haisong Zhang, Hao Wang, Wenming Wang, Lixin Jia, Lihang Feng and Dong Wang
Electronics 2024, 13(22), 4469; https://doi.org/10.3390/electronics13224469 - 14 Nov 2024
Viewed by 357
Abstract
Unmanned autonomous systems (UASs), including drones and robotics, are widely employed across various fields. Despite significant advances in AI-enhanced intelligent systems, there remains a notable deficiency in the interpretability and comprehensive quantitative evaluation of these systems. The existing literature has primarily focused on [...] Read more.
Unmanned autonomous systems (UASs), including drones and robotics, are widely employed across various fields. Despite significant advances in AI-enhanced intelligent systems, there remains a notable deficiency in the interpretability and comprehensive quantitative evaluation of these systems. The existing literature has primarily focused on constructing evaluation frameworks and methods, but has often overlooked the rationality and reliability of these methods. To address these challenges, this paper proposes an innovative optimization evaluation method for data-driven unmanned autonomous systems. By optimizing the weights of existing indicators based on data distribution characteristics, this method enhances the stability and reliability of assessment outcomes. Furthermore, interpretability techniques such as Local Interpretable Model-agnostic Explanations (LIMEs) and Partial Dependence Plots (PDPs) were employed to verify the effectiveness of the designed evaluation indicators, thereby ensuring the robustness of the evaluation system. The experimental results validated the effectiveness of the proposed approach. Full article
(This article belongs to the Special Issue Advanced Control Strategies and Applications of Multi-Agent Systems)
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15 pages, 1598 KiB  
Article
Asynchronous Sliding Mode Control of Networked Markov Jump Systems via an Asynchronous Observer Approach Based on a Dynamic Event Trigger
by Jianping Deng, Haocheng Lou and Baoping Jiang
Electronics 2024, 13(21), 4182; https://doi.org/10.3390/electronics13214182 - 25 Oct 2024
Viewed by 536
Abstract
This paper explores the utilization of sliding mode control, which relies on an asynchronous observer, for Markov jump systems subject to external disturbances. Firstly, given that the system’s mode is not directly measurable and could potentially differ from the observer’s and controller’s mode, [...] Read more.
This paper explores the utilization of sliding mode control, which relies on an asynchronous observer, for Markov jump systems subject to external disturbances. Firstly, given that the system’s mode is not directly measurable and could potentially differ from the observer’s and controller’s mode, the paper constructs an asynchronous observer employing a hidden Markov model. Secondly, a sliding surface is designed to correspond with the asynchronous observer. Moreover, a multi-parameter event-triggered mechanism is incorporated into the observer design to alleviate bandwidth strain. Thirdly, by applying the integrated sliding mode control law, we ensure that the system state trajectories will reach the sliding surface within a finite time. Fourthly, the achievement of H∞ stability is realized by making use of the Lyapunov function. Lastly, a practical-oriented example is presented to illustrate the efficiency of the established method. Full article
(This article belongs to the Special Issue Advanced Control Strategies and Applications of Multi-Agent Systems)
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19 pages, 3061 KiB  
Article
Improved Control Strategy for Dual-PWM Converter Based on Equivalent Input Disturbance
by Zixin Huang, Wei Wang, Chengsong Yu and Junjie Lu
Electronics 2024, 13(18), 3777; https://doi.org/10.3390/electronics13183777 - 23 Sep 2024
Viewed by 660
Abstract
Aiming at the problems of jittering waveforms and poor power quality caused by external disturbances during the operation of a dual-pulse-width-modulation (PWM) converter, an improved terminal sliding mode control and an improved active disturbance rejection control (ADRC) are investigated. The method is based [...] Read more.
Aiming at the problems of jittering waveforms and poor power quality caused by external disturbances during the operation of a dual-pulse-width-modulation (PWM) converter, an improved terminal sliding mode control and an improved active disturbance rejection control (ADRC) are investigated. The method is based on mathematical models of grid-side and machine-side converters to design the controllers separately, and the balance between the two sides is maintained by the capacitor voltage. An improved terminal fuzzy sliding mode control and equivalent input disturbance (EID)-error-estimation-based active disturbance rejection control are presented on the grid side to improve the voltage response rate, and an improved support vector modulation (SVM)–direct torque control (DTC)–ADRC method is developed on the motor side to improve the robustness against disturbances. Finally, theoretical simulation experiments are built in MATLAB R2023a/Simulink to verify the effectiveness and superiority of this method. Full article
(This article belongs to the Special Issue Advanced Control Strategies and Applications of Multi-Agent Systems)
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