Zhenzhong Xu

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.

Aiming at the traditional single sensor vibration signal cannot fully express the bearing running state, and in the high noise background, the traditional algorithm is insufficient for fault feature extraction. This paper proposes a fault diagnosis algorithm based on multi-sensor and hybrid multimodal feature fusion to achieve high-precision fault diagnosis by leveraging the operating state information of bearings in a high-noise environment to the fullest extent possible. First, the horizontal and vertical vibration signals from two sensors are fused using principal component analysis, aiming to provide a more comprehensive description of the bearing’s operating condition, followed by data set segmentation. Following fusion, time-frequency feature maps are generated using a continuous wavelet transform for global time-frequency feature extraction. A first diagnostic model is then developed utilizing a residual neural network. Meanwhile, the feature data is normalized, and 28 time-frequency feature indexes are extracted. Subsequently, a second diagnostic model is constructed using a support vector machine. Lastly, the two diagnosis models are integrated to derive the final model through an ensemble learning algorithm fused at the decision level and complemented by a genetic algorithm solution to improve the diagnosis accuracy. Experimental results demonstrate the effectiveness of the proposed algorithm in achieving superior diagnostic performance with a 97.54% accuracy rate.