Digital Twin-Driven Decision-Making Support Approach in Adaptive Control of Production Systems

The development and applications of digital twin-driven Decision Support Systems (DSS) are discussed. In order to combine the advantages and features of model-driven, data-driven, knowledge-driven, documentation-driven and communication-driven concepts a hybrid approach for DSS design and development is proposed. Basic methods and paradigms of the concepts mentioned above are applied in implementation of hybrid approach. This approach is based on application of digital twins, adaptive digital twins in particular, for solving adaptive control problems in production systems context. A conceptual model of the architecture of adaptive digital twins of production systems is presented. The architecture of the adaptive digital twin has a hierarchical structure and is divided into three levels: 1) visualization and control level (visualization subsystem - human-machine interface; automated control subsystem); 2) simulation and optimization level (simulation subsystem (adaptive multi-agent simulation model; machine learning module); optimization subsystem); 3) data processing and storage level (subsystem of data acquisition, processing, aggregation and storage; data mining subsystem; big data analysis subsystem). The proposed concept for the development of adaptive digital twins is based on the application of an integrated approach combining methods of simulation, artificial intelligence, optimization, decision-making support, control theory, data processing, storage and analysis (including big data), etc.