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Publications & Journals

Relaxing Platform Dependencies in Agent-Based Control Systems

Citation:

Perez Hernandez, M. E., McFarlane, D., Parlikad, A., Herrera Fernandez, M. H., & Jain, A. (2021). Relaxing Platform Dependencies in Agent-Based Control Systems. IEEE Access, 9 30511-30527

Published:

01/01/2021

Link:

University:

University of Cambridge

Abstract: 

Agent-based systems have been widely used to develop industrial control systems when they are required to address issues such as flexibility, scalability and portability. The most common approach to develop such control systems is with agents embedded in a platform that provides software libraries and runtime services that ease the development process. These platforms also bring challenges to the agent-based control system engineering. Firstly, they influence the control system design, for example, by assuming the need of a global directory of agents even if this is not required. Hence, introducing unnecessary overhead to the control system that can worsen when it grows. Secondly, as agents are embedded in the platform, it also constraints the deployment of agents across available edge, fog and cloud computing infrastructures. This paper addresses these challenges through an approach to build agent-based control systems, that relaxes the dependencies in multiagent system (MAS) platforms, through the use of container-based virtualisation. This approach enables the implementation of agents as self-contained applications that can be deployed in independent environments but still are able to communicate and coordinate with other agents of the control system. We built a prototype and evaluated this approach in the context of a case study for the supervisory control of digital network infrastructures. This case study enabled us to demonstrate feasibility of the approach and to show the flexibility, of the resulting control system, to adopt several topologies as well as to operate at different scales, over emulated networks. We also concluded that designing agents as individual deployment units is also cost-effective especially in control scenarios with low number of stable agents

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