Mathematical Methods Applied in Artificial Intelligence and Multi-Agent Systems, 2nd Edition

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: 20 May 2025 | Viewed by 1180

Special Issue Editors


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Guest Editor
School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: distributed control and optimization; complex systems and control
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: multiagent systems; reinforcement learning; robot control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Due to rapid developments in computing, communication and sensing technology, multi-agent systems have become increasingly ubiquitous in real life. Their applications include mobile sensor networks, autonomous vehicle formations, intelligent transportation systems, smart grids, etc. The complex unknown environment and inaccurate dynamics pose additional challenges in the design of system modeling, control and optimization of such systems. Therefore, data science and machine learning are providing opportunities to develop artificial intelligence-based methods and enable new control and optimization paradigms for multi-agent systems.

The aim of this Special Issue is to highlight significant developments at the interface of machine learning, dynamics and control systems. Original papers addressing the aforementioned challenges and opportunities are especially welcome.

Potential topics include, but are not limited to, the following:

  • Data-driven modeling and system identification of multi-agent systems;
  • Reinforcement learning control and optimization of multi-agent systems;
  • Intelligent learning and adaptive control of multi-agent systems;
  • Robust distributed control methods;
  • Sampled data and event-triggered intelligent control;
  • Online-distributed optimization and coordination;
  • Distributed intelligent control and optimization applications.

Prof. Dr. Jiangping Hu
Dr. Zhinan Peng
Guest Editors

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Keywords

  • machine learning
  • multi-agent systems and control
  • distributed optimization
  • robust and intelligent control
  • data-based model identification

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Related Special Issue

Published Papers (2 papers)

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Research

19 pages, 1092 KiB  
Article
Synchronization of Multi-Agent Systems Composed of Second-Order Underactuated Agents
by Branislav Rehák, Anna Lynnyk and Volodymyr Lynnyk
Mathematics 2024, 12(21), 3424; https://doi.org/10.3390/math12213424 - 31 Oct 2024
Viewed by 444
Abstract
The consensus problem of a multi-agent system with nonlinear second-order underactuated agents is addressed. The essence of the approach can be outlined as follows: the output is redesigned first so that the agents attain the minimum-phase property. The second step is to apply [...] Read more.
The consensus problem of a multi-agent system with nonlinear second-order underactuated agents is addressed. The essence of the approach can be outlined as follows: the output is redesigned first so that the agents attain the minimum-phase property. The second step is to apply the exact feedback linearization to the agents. This transformation divides their dynamics into a linear observable part and a non-observable part. It is shown that consensus of the linearizable parts of the agents implies consensus of the entire multi-agent system. To achieve the consensus of the original system, the inverse transformation of the exact feedback linearization is applied. However, its application causes changes in the dynamics of the multi-agent system; a way to mitigate this effect is proposed. Two examples are presented to illustrate the efficiency of the proposed synchronization algorithm. These examples demonstrate that the synchronization error decreases faster when the proposed method is applied. This holds not only for the states constituting the linearizable dynamics but also for the hidden internal dynamics. Full article
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14 pages, 1035 KiB  
Article
Distributed Disturbance Observer-Based Containment Control of Multi-Agent Systems with Event-Triggered Communications
by Lin Hu and Long Jian
Mathematics 2024, 12(19), 3117; https://doi.org/10.3390/math12193117 - 5 Oct 2024
Viewed by 488
Abstract
This article investigates a class of multi-agent systems (MASs) with known dynamics external disturbances, where the communication graph is directed, and the followers have undirected connections. To eliminate the impacts of external disturbance, the technologies of disturbance observer-based control are introduced into the [...] Read more.
This article investigates a class of multi-agent systems (MASs) with known dynamics external disturbances, where the communication graph is directed, and the followers have undirected connections. To eliminate the impacts of external disturbance, the technologies of disturbance observer-based control are introduced into the containment control problems. Additionally, to save communication costs and energy consumption, a distributed disturbance observer-based event-triggered controller is employed to achieve containment control and reject disturbance. Furthermore, designing the event-triggered function using an exponential function is beneficial for a time-dependent term while ensuring the exclusion of Zeno behavior. Finally, a numerical simulation is provided to validate the effectiveness of the theoretical analysis. Full article
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