How Digital Twins Support Control System Development
Read the full article to see how this approach is being applied in high-performance engineering environments.
Published
14 NOV 2025
Est. reading time
2 min
Digital twins provide a powerful framework for developing and optimising control systems across the entire lifecycle of an asset. A digital twin is a virtual representation of a physical system that remains connected to its real-world counterpart through live or near real-time data. This connection allows the twin to mirror actual operating conditions, enabling real-time monitoring, predictive analytics and informed decision-making during operation.
In the development phase, engineers typically start with high-fidelity models or virtual prototypes. These models simulate system dynamics under a wide range of conditions, supporting control design, tuning and validation.
They enable extensive testing through simulation and integration of real components using Software-in-the-Loop and Hardware-in-the-Loop techniques. While these models are not yet full digital twins because they lack live data, they form the foundation for the operational twin.
This virtual testing process significantly reduces the time and cost associated with physical validation. It also allows engineers to investigate edge cases and optimise control performance well before deployment.
Once deployed, the model can be connected to live sensor data, transforming it into a true digital twin. Running in parallel with the physical asset, the twin validates control performance, detects deviations, predicts faults and supports optimisation throughout the system’s life.
With this live connection, control strategies can be continuously updated to match real-world operating conditions. Maintenance decisions, performance tuning and fault diagnostics all benefit from the insight the digital twin provides.
By combining accurate modelling during development with real-time data during operation, digital twins create a closed-loop bridge between design and in-service performance. This reduces risk, accelerates validation and ensures control systems remain effective as conditions change.
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