Due to the inherent structural complexity, dynamic behavior, and strong nonlinearities of interconnected systems, conventional control and existing data-driven methods such as T–S fuzzy models and SINDy often struggle to simultaneously ensure modeling accuracy, structural interpretability, and real-time adaptability under dynamic operation modes. To address these limitations, this paper proposes an affine-structured performance-controllable neural network guided backstepping predictive control framework. Unlike conventional black-box data-driven models, the proposed approach embeds the affine nonlinear system structure into the neural network and adopts a Lyapunov-based training strategy, enabling both accurate dynamic approximation and explicit control law design. Meanwhile, a backstepping predictive control scheme is developed to effectively handle interconnection-induced coupling and multivariable constraints with reduced computational burden. Furthermore, a performance-controllable integrated neural network adaptive update method is introduced by reformulating model adaptation as a control problem, allowing near-real-time model updating using only finite data and guaranteeing stable and rapid convergence of prediction errors. Rigorous theoretical analysis and extensive experimental results demonstrate that the proposed method achieves superior control performance under dynamic operation modes.

Data-driven anomaly detection aims to learn a decision boundary, enveloping the normal region, and separating normal data from abnormal data. However, industrial data are fairly complex due to varying feedstock and unclear transfer processes and chemical reactions. This means the decision boundary will be very complex and even intractable. In addition, process variables are high-dimensional in modern industrial processes, which strengthens the difficulty of boundary extraction. Generally, the boundary should exactly exceed the outermost samples for precisely drawing normal regions. However, what we have in most situations is just normal data contaminated by unknown noises. Hence, conventional solutions that use statistical analysis to define a normal region result in a not-so-accurate decision boundary where missing alarms occur frequently. In addition to the conventional solution based entirely on historical data, i.e., passive fault detection (PAD), an alternative detection method, active fault detection (AAD), can circumvent the above problem by stimulating system performance through the intervention of auxiliary signal. While it results in disruption of the normal operation conditions for the process, its method to enhance output performance through additional signals inspires us. In this paper, we resort to the ability of deep neural networks to fit nonlinear data and perform dimension reduction. A fault data generation strategy is proposed and the artificially generated fault data are used to regulate the model training. The new virtual fault data aids in suppressing the decision boundary closest to the outermost periphery. We propose the principles of data generation and form a network structure, implementing information fusion of genuine normal samples and virtual fault samples. Two cases demonstrate the efficiency of the proposed method.

In response to the challenge of strongly nonlinear and multimode systems control, this paper introduces a weighted deep learning based adaptive predictive control method. This approach integrates LSTM networks for different operating modes using a set of weighting coefficients. These coefficients are dynamically updated during online control via an error-guided scheduling strategy to adapt to changing operation modes. Compared to offline identification based methods, the proposed method eliminates the need for mode recognition or model switching strategies and can adapt to drifted operation modes. In contrast to online methods, it achieves rapid model convergence and reduced computational cost, requiring only minimal data to update the weighting coefficients without necessitating the retraining of the LSTM networks. Theoretical convergence and stability analysis ensure the reliability of the proposed method. Numerical simulations and industrial control experiments demonstrate that the proposed approach exhibits favorable control performance across both known and drifted operation modes. Note to Practitioners—Considering the changing operation modes in complex industrial processes and the detrimental effect of slow or unstable control during system operation, this paper proposes a weighted LSTM based predictive control method for strongly nonlinear and multimode systems. Extensive experiments demonstrate that compared to other state-of-the-art methods, this method can rapidly adapt to changes in operating modes with a small amount of data, meeting both real-time and stability requirements for online control.

Non-ferrous metals, as important basic raw materials, are the strategic supports for national economic development. For non-ferrous metal smelting enterprises, raw material procurement is the focal and most important session. Due to the fluctuation of production volumes and the future changes in raw-material prices, the procurement cost of raw materials is high and with a high risk of shortage. In this paper, we propose a multi-period rolling robust procurement model considering price and demand uncertainties. In particular, we design a data-driven method to construct the budget-based uncertainty sets and derive the robust counterpart of the robust procurement model. Comparative experiments on the real data with classic and advanced procurement policies show that our proposed solution approach achieves the lowest cost under the premise of continuous supply of raw materials. Interestingly, we observe that limited capital and warehouse capacity can effectively restrain unreasonable behavior and thus not to cause big losses in uncertain environments. In addition, a relatively long planning horizon can be counterproductive. These valuable and actionable insights can well guide practical decision-making.Note to Practitioners—For the raw material procurement of non-ferrous metal smelter, this article proposes a multi-period rolling robust procurement model considering price and demand uncertainties. Taking account of the dynamic characteristics of raw-material prices and the seasonal characteristics of raw-material demands, a data-driven method to construct budget-based uncertainty sets is designed. In particular, we derive the solvable robust counterpart of the robust procurement model. The proposed approach can reduce costs ensuring the continuous supply of raw materials. Some interesting and actionable managerial insights are obtained that can well guide practical decision-making, and the proposed data-driven approach is realizable.

Deep neural networks (DNNs) as one of the key enabling technologies have been widely used in industrial artificial intelligence (IAI). However, recent research has revealed that they are quite vulnerable to adversarial attacks, arousing serious concerns about DNNs' robustness in many IAI-driven applications such as industrial video analysis tasks. Considering the attack efficiency and effectiveness, it is essential to study the sparse adversarial attack examples. Nevertheless, current methods' performance is limited by insufficient sparsity and lacks a unified framework. To solve these problems, in this article, we focus on sparse adversarial video attacks and propose a dual-branch neural network-based model to generate sparse adversarial video examples in an end-to-end fashion. We conduct extensive experiments with mainstream video models on public datasets and industrial case. Experimental results demonstrate that compared with state-of-the-art methods, our method can achieve a faster and better attacking performance with less than 1% perturbed pixels in the video.

With the digital transformation of process manufacturing, identifying the system model from process data and then applying to predictive control has become the most dominant approach in process control. However, the controlled plant often operates under changing operating conditions. What is more, there are often unknown operating conditions such as first appearance operating conditions, which make traditional predictive control methods based on identified model difficult to adapt to changing operating conditions. Moreover, the control accuracy is low during operating condition switching. To solve these problems, this article proposes an error-triggered adaptive sparse identification for predictive control (ETASI4PC) method. Specifically, an initial model is established based on sparse identification. Then, a prediction error-triggered mechanism is proposed to monitor operating condition changes in real time. Next, the previously identified model is updated with the fewest modifications by identifying parameter change, structural change, and combination of changes in the dynamical equations, thus achieving precise control to multiple operating conditions. Considering the problem of low control accuracy during the operating condition switching, a novel elastic feedback correction strategy is proposed to significantly improve the control accuracy in the transition period and ensure accurate control under full operating conditions. To verify the superiority of the proposed method, a numerical simulation case and a continuous stirred tank reactor (CSTR) case are designed. Compared with some state-of-the-art methods, the proposed method can rapidly adapt to frequent changes in operating conditions, and it can achieve real-time control effects even for unknown operating conditions such as first appearance operating conditions.

Smart manufacturing has become mainstream in the development of manufacturing industry, where Industrial Internet of Things plays a critical role. In this article, a systematic intelligent technique for procurement supply chain (PSC) optimization is proposed. In this technique, an integrated approach based on variational mode decomposition and long short-term memory network is used to predict the market price. Considering the factors, such as production plan and market fluctuation, a multiperiod dynamic purchasing model is built. A stacked autoencoder under bootstrap aggregation is then trained to evaluate suppliers automatically end-to-end based on various data. Finally, a multiobjective order allocation model is established considering the procurement costs and supplier scores, and solved by particle swarm optimization. The extensive experiments are performed using a realistic industrial application in a zinc smelter company. The experimental results demonstrate that the proposed technique greatly reduces labor costs, improves the efficiency of PSC, and reduces the procurement costs of the company.

In real industrial processes, factors, such as the change in manufacturing strategy and production technology lead to the creation of multimode industrial processes and the continuous emergence of new modes. Although the industrial SCADA system has accumulated a large amount of historical data, which can be used for modeling and monitoring multimode processes to a certain extent, it is difficult for the model learned from historical data to adapt to emerging modes, resulting in the model mismatch. On the other hand, updating the model with data from new modes allows the model to continuously match the new modes, but it may cause the model to lose the ability to represent the historical modes, resulting in "catastrophic forgetting." To address these problems, this article proposed a jointly mode-matching and similarity-preserving dictionary learning (JMSDL) method, which updated the model by learning the data of new modes, so that the model can adaptively match the newly emerged modes. At the same time, a similarity metric was put forward to guarantee the representation ability of the proposed method for historical data. A numerical simulation experiment, the CSTH process experiment, and an industrial roasting process experiment indicated that the proposed JMSDL method can match new modes while maintaining its performance on the historical modes accurately. In addition, the proposed method significantly outperforms the state-of-the-art methods in terms of fault detection and false alarm rate.