Distributed Optimization: Challenges and Applications
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".
Deadline for manuscript submissions: 20 September 2024 | Viewed by 318
Special Issue Editor
Interests: distributed estimation and control; fractional-order learning systems; optimization; machine learning; high-dimensional algebras for control and signal processing applications
Special Issue Information
Dear Colleagues,
Recent years have borne witness to the proliferation of modern sensors and actuation equipment with ever-increasing processing power and built-in communication capabilities. As a direct result, most modern monitoring and control systems, generally referred to as multi-agent networked systems or multi-agent systems for short, have come to be characterised as multiple autonomous agents interacting over an ad hoc network to achieve a common goal. Moreover, an increasing number of envisioned engineering applications, such as autonomous vehicle navigation, smart power/gas distribution systems, and control of robot swarms, are starting to resemble multi-agent systems, thus highlighting the importance of understating their behaviour and design parameters.
Although initial studies on using multi-agent systems have shown great promise, traditional learning, estimation, and control techniques are based on optimisation techniques derived with a single processing agent. This Special Issue focuses on fully distributed optimisation techniques derived to enable multi-agent systems to observe, learn, and implement optimal decisions to achieve a common goal. Moreover, applications of these distributed optimisation techniques in modelling social behaviour of robots and humans, internet of things (IoT), smart energy distribution systems, federated learning, distributed estimation, decentralised control, and autonomous vehicles are of particular interest.
Dr. Sayed Pouria Talebi
Guest Editor
Manuscript Submission Information
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Keywords
- distributed optimisation
- federated learning
- decentralised and cooperative control
- information processing over networks
