In industrial processes, mismatches between models and actual systems often degrade the performance of Model Predictive Control (MPC), potentially leading to instability or safety violations under dynamic operating conditions. To address this challenge, the paper introduces a hybrid control architecture named Trans-Tube-MPC, which leverages Transformer-based temporal modeling and tube-based robust constraints to enhance the robustness of the control system against model failures. The approach employs a Transformer network trained on closed-loop operational data to predict and compensate for state deviations caused by disturbances, while adaptive tube constraints dynamically adjust prediction boundaries to mitigate the risk of overcorrection. The innovation of this method lies in the introduction of a dynamically adjusted tube width, which adapts based on the prediction discrepancy between the Transformer model and the state-space model, thus allowing the control system to remain robust even in the face of model failures. Experimental studies demonstrate that the Trans-Tube-MPC framework can maintain control performance under significant model parameter deviations where conventional MPC would fail. The proposed method provides an effective solution to the problem of model mismatch and prediction error and shows significant advantages in dealing with control issues under model failure conditions, establishing a new way to reconcile data-driven adaptability with the reliability of control systems.
Chen et al. (Fri,) studied this question.