Semantically Enhanced Online Configuration of Feedback Control Schemes

• Published in: IEEE transactions on cybernetics Vol. 48; no. 3; pp. 1081 - 1094
• Main Authors: Milis, Georgios M., Panayiotou, Christos G., Polycarpou, Marios M.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01-03-2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Architecture; Computer architecture; Configurations; Control systems; Cyber-physical systems; Feedback control; Inference; Internet of Things (IoT); Interoperability; Performance measurement; Redesign; semantic composition; semantic knowledge models; Semantics; Smart buildings; System effectiveness
• ISSN: 2168-2267; 2168-2275
• DOI: 10.1109/TCYB.2017.2680740

Recent progress toward the realization of the "Internet of Things" has improved the ability of physical and soft/cyber entities to operate effectively within large-scale, heterogeneous systems. It is important that such capacity be accompanied by feedback control capabilities sufficient to ensure that the overall systems behave according to their specifications and meet their functional objectives. To achieve this, such systems require new architectures that facilitate the online deployment, composition, interoperability, and scalability of control system components. Most current control systems lack scalability and interoperability because their design is based on a fixed configuration of specific components, with knowledge of their individual characteristics only implicitly passed through the design. This paper addresses the need for flexibility when replacing components or installing new components, which might occur when an existing component is upgraded or when a new application requires a new component, without the need to readjust or redesign the overall system. A semantically enhanced feedback control architecture is introduced for a class of systems, aimed at accommodating new components into a closed-loop control framework by exploiting the semantic inference capabilities of an ontology-based knowledge model. This architecture supports continuous operation of the control system, a crucial property for large-scale systems for which interruptions have negative impact on key performance metrics that may include human comfort and welfare or economy costs. A case-study example from the smart buildings domain is used to illustrate the proposed architecture and semantic inference mechanisms.