AI-Enabled Digital Twin Framework for Prediction and Adaptive Control of Industrial Systems under Uncertainty

The increasing complexity of Industry 4.0 and Industry 5.0 environments has accelerated the adoption of Digital Twin (DT) technologies for real-time monitoring, predictive maintenance, and operational optimization of industrial systems. Despite significant advances in AI-enabled Digital Twins, most existing frameworks remain focused on monitoring and prediction tasks, while limited attention has been given to transforming predictive insights into adaptive control actions under uncertain and dynamically changing operating conditions. Moreover, uncertainty associated with AI-driven predictions is often overlooked, reducing the reliability and trustworthiness of decision-making processes in safety-critical industrial environments.

This paper presents a conceptual AI-enabled Digital Twin framework for adaptive prediction and control of industrial systems under uncertainty. The proposed framework integrates Industrial Internet of Things (IIoT)-based data acquisition, real-time Digital Twin synchronization, AI-driven predictive analytics, uncertainty-aware forecasting, adaptive decision-making, and closed-loop control within a unified architecture. Unlike conventional Digital Twin approaches that terminate at prediction or decision-support stages, the proposed framework introduces a prediction-to-control paradigm in which predictive outcomes are continuously evaluated, interpreted under uncertainty, and translated into adaptive control actions through a feedback-driven learning mechanism.

The principal contribution of this work is an architectural innovation that integrates predictive intelligence, uncertainty evaluation, adaptive decision-making, and closed-loop control within a continuously evolving Digital Twin ecosystem. This integration enables the transition from predictive Digital Twins to adaptive and self-improving intelligent Digital Twin systems. By incorporating uncertainty-aware intelligence into the decision loop, the proposed framework has the potential to improve prediction reliability, operational resilience, resource efficiency, and sustainability in complex industrial environments. Furthermore, the proposed architecture establishes a foundation for future experimental validation and deployment of trustworthy, resilient, and autonomous Digital Twin-driven industrial automation systems aligned with the vision of Industry 5.0.