Uncertainty-Aware Digital Twins: Robust Model Predictive Control Using Time-Series Deep Quantile Learning

Abstract Digital twins, virtual replicas of physical systems that enable real-time monitoring, model updates, predictions, and decision-making, present novel avenues for proactive control strategies for autonomous systems. However, achieving real-time decision-making in digital twins considering uncertainty necessitates an efficient uncertainty quantification (UQ) approach and optimization driven by accurate predictions of system behaviors, which remains a challenge for learning-based methods. This article presents a simultaneous multistep robust model predictive control (MPC) framework that incorporates real-time decision-making with uncertainty awareness for digital twin systems. Leveraging a multistep-ahead predictor named time-series dense encoder (TiDE) as the surrogate model, this framework differs from conventional MPC models that provide only one-step-ahead predictions. In contrast, TiDE can predict future states within the prediction horizon in one shot, significantly accelerating MPC. Furthermore, quantile regression is employed with the training of TiDE to perform flexible and computationally efficient UQ on data uncertainty. Consequently, with the deep learning quantiles, the robust MPC problem is formulated into a deterministic optimization problem and provides a safety buffer that accommodates disturbances to enhance the constraint satisfaction rate. As a result, the proposed method outperforms existing robust MPC methods by providing less conservative UQ and has demonstrated efficacy in an engineering case study involving directed energy deposition (DED) additive manufacturing. This proactive, uncertainty-aware control capability positions the proposed method as a potent tool for future digital twin applications and real-time process control in engineering systems.