Learning-Based Control of Networked Systems
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".
Deadline for manuscript submissions: 1 January 2027 | Viewed by 68
Special Issue Editor
Interests: networked control systems; learning based control
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Networked control systems (NCS) are widely encountered in modern engineering applications such as power systems, intelligent transportation, industrial automation, and unmanned swarm systems. In these systems, control loops are closed through communication networks, which introduce challenges including delays, packet losses, bandwidth constraints, and cyber security risks. Traditional analysis and design methods for NCS are primarily model-based, relying on accurate mathematical models and rigorous control-theoretic tools to ensure stability, robustness, and performance.
With the rapid development of embedded hardware, sensing technologies, and machine learning techniques, increasing attention has shifted toward data-driven and learning-based approaches for networked control systems. These emerging paradigms enable control design and performance optimization directly from data, alleviating the need for precise system models and offering improved adaptability in complex and uncertain environments.
This Special Issue aims to bring together recent advances in modeling, analysis, and control of networked control systems from a data-driven and learning-based perspective. Topics of interest include, but are not limited to, finite-sample system identification and control, learning-based stability and performance analysis, reinforcement learning for networked control, adaptive and online control over networks, distributed and cooperative learning in multi-agent systems, and integration of model-based and data-driven methods. Contributions addressing theoretical foundations, algorithm development, and practical applications are all welcome.
By bridging control theory and modern machine learning, this Special Issue seeks to promote new methodologies and insights for the next generation of intelligent and resilient networked control systems.
Prof. Dr. Liang Xu
Guest Editor
Manuscript Submission Information
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Keywords
- networked control systems
- data-driven control
- reinforcement learning
- system identification
- distributed control
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