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Machine learning performs well in many problems. However, the tendency to generate predictions that violate theoretical knowledge makes it difficult to apply to practical processing. To resolve this situation, this paper combines domain knowledge with a data-driven model, proposes a theory-guided machine learning framework based on a parameter transfer strategy, and applies it to the width prediction of plates after multiple passes of hot rolling. The framework applies a swarm optimization algorithm to the original theoretical model and generates numerous highly-physical consistent samples. The established deep neural network (DNN) model is trained with simulated data, and the parameters are fine-tuned using a parameter transfer strategy combined with actual data to ensure excellent adaptation to the actual environment based on adequate learning of theoretical knowledge. In tests, the proposed model had the best overall prediction performance in this paper. Meanwhile, the developed model is consistent with the existing perception of rolling theory. This allows for the quick and reliable application of machine learning models in production., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 ISA. Published by Elsevier Ltd. All rights reserved.)

The control precision of thickness and tension is a crucial indicator for evaluating a tandem cold rolling control system. However, the control mode for field application cannot meet the actual quality requirements. Therefore, a distributed model predictive control (DMPC) strategy combined with neighborhood optimization is proposed to decrease the strip thickness deviation and tension change in this paper. First, a cold rolling model describing the relationship among the process parameters is established for the multi-stand cold rolling system. Then, according to the neighborhood optimization theory, the state evolution equation of the output system on each stand is derived. Furthermore, through proper consideration of the input and state information during optimization, optimal control variables are obtained using the proposed performance index to improve the system performance. A series of simulations were carried out with actual rolling data to analyze and validate the capability of the designed control system. The statistical data show that as roll speed disturbance occurs, the thickness and tension deviations can be controlled within respective ranges of 6 × 10 -5 mm and 0.012 kN with the DMPC control strategy. In addition, each scan cycle calculation only takes 0.0085 s in such a strategy. Compared with the conventional control method, the thickness and tension DMPC control system provides excellent performance and can effectively enhance the strip product quality., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 ISA. Published by Elsevier Ltd. All rights reserved.)

This article studies the problem of the optimal stealth attack strategy design for linear cyber-physical systems (CPSs). Virtual systems that reflect the attacker's target are constructed, and a linear attack model with varying gains is designed based on the virtual models. Unlike the existing optimal stealth attack strategies that are designed based on sufficient conditions, necessary and sufficient conditions are, respectively, established to achieve the optimal attack performance while maintaining stealth in virtue of the solvability of certain coupled recursive Riccati difference equations (RDEs). Under those conditions, an optimal stealth attack strategy is constructed by an offline algorithm. A simulation example is applied to verify the effectiveness of the presented technical scheme.