Abstract

The advantages of using collaborative and distributed systems to control and optimise industrial environments have been explored in recent years. However, the self-organised and dynamic approaches that deliver these same advantages bring challenges associated with the long-term unpredictability of these approaches. The proposed work aims to present a framework that integrates a Digital Twin-based Optimiser within these systems to predict the system's evolution and reconfigure it if required. Digital Twins can be an essential advantage in adopting these systems due to the possibility of carrying out simulations at an accelerated speed and reconfiguring the self-organised control system.

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