Digital Twins Enabled Smart Control Engineering

Dear Colleagues,

Digital twins (DTs), artificial intelligence, and big data are among the technologies that enable and drive digital transformation in industry and academia. They provide a virtual representation of complex systems, combining physical-first-principle, data-driven, or AI models. Thus, digital twins allow the development of enabling capabilities in feedback control systems, including fault detection, remaining useful life (RUL), or self-optimizing control, contributing towards creating resilient, self-aware systems.

This Special Issue, titled “Digital Twins Enabled Smart Control Engineering”, will look at theoretical and practical perspectives on integrating digital twins into feedback control theory and leveraging the enhanced system knowledge provided by DTs to design and implement smart control systems.

The main goal of this Special Issue is to demonstrate how digital twins are integrated into control theory to create smart control systems.

Any type of industrial application is encouraged, so long as digital twins could play a significant role in improving the process performance and have the potential to enable smart capabilities. Possible applications include mechatronics, autonomous vehicles, battery modelling and state of charge (SoC), robotics, process control, semiconductors, and manufacturing processes.

Topics of interest include, but are not limited to, the following:

• Using digital twins to design data-driven or model-based control systems;
• Leveraging digital twins as a tool to enable parallel computing capabilities to accelerate AI training and deployment;
• Self-optimizing control methods supported by digital twins that enable the systematic self-awareness of process changes, alarms, and uncertainties;
• Integrating predictive maintenance and remaining useful life analysis into digital twins to create RUL-informed feedback control systems;
• Practical implementation of digital twins in monitoring physical assets based on embedded, edge, or cloud architectures;
• Design of robust/optimal/adaptive control systems using digital twins for uncertainty quantification;
• Digital twins in the design of fault-tolerant controllers and expert systems

Dr. Jairo Viola
Guest Editor

Manuscript Submission Information

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• digital twin
• self-optimizing control
• fault detection
• remaining useful life
• predictive maintenance
• process control
• mechatronics