Distributed Learning and Control in Multi-Agent and Robotic Systems: Methodologies, Applications, and Future Trends
A Special Issue of Electronics (ISSN 2079-9292) belonging to the section "Artificial Intelligence".
Deadline for manuscript submissions: 15 November 2026 | Viewed by 687
Editors
Interests: multi-agent systems; machine learning; control theory; robotics; data representation
Interests: swarm intelligence; swarm intelligence optimization algorithm; fireworks algorithm; swarm robotics; machine learning and data mining
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The distributed control of multi-agent systems has emerged as a fundamental paradigm for coordinating complex networks of autonomous agents, ranging from robotic swarms to smart grid systems. As modern applications increasingly rely on collaborative autonomous systems, the need for efficient, scalable, and robust distributed control strategies has become paramount. This Special Issue will focus on innovative theoretical frameworks, algorithms, and practical implementations for the distributed control of multi-agent systems and robots.
Topics of interest include, but are not limited to, the following:
- Distributed optimization and coordinated control of multi-agent systems;
- Reinforcement learning and adaptive control in multi-agent environments;
- Fault-tolerant and resilient control systems for multi-agent networks;
- Cooperative and competitive multi-agent systems with game-theoretic approaches;
- Distributed estimation and localization in multi-robot systems;
- Communication-efficient control protocols for sparse networks;
- Heterogeneous agent collaboration and coordination strategies;
- Smart grid applications with the distributed control of renewable energy sources;
- Autonomous vehicle coordination and traffic management systems;
- Privacy preservation in distributed multi-agent networks.
Dr. Zewen Yang
Prof. Dr. Ying Tan
Guest Editors
Manuscript Submission Information
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Keywords
- distributed learning
- multi-agent control
- multi-agent reinforcement learning
- decentralized coordination
- cooperative robotics
- consensus algorithms
- federated learning
- distributed optimization
- networked control systems
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