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2024 China automation Congress, CAC 2024

作者： Xi, Chao Wu, Gaochang Fan, Zizhu Xu, Feng Wang, Hui Tang, Xiaokun School of Electrical and Automation Engineering East China Jiaotong University Nanchang China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China College of Computer Science and Technology Shanghai University of Electric Power Shanghai China Key Laboratory of Advanced Control and Optimization of Jiangxi Province East China Jiaotong University Nanchang China

ISBN: (纸本)9798350368604

The smelting process of the fused magnesium furnace(FMF) is prone to semi-molten abnormal conditions, which may lead to low product quality and production efficiency, and is also prone to safety accidents. At present, the operating conditions of FMF mainly rely on manual observation of the furnace shell features for diagnosis, and it is hard to timely and effectively identify abnormalities in the smelting process. Given the difficulty in identifying abnormal operating conditions of FMF, this paper proposes a deep multiview fuzzy classification method(DMFC) that can automatically identify abnormal operating conditions. First, based on the three-phase current data, CNN and Transformer are used to extract features as multiple views. Secondly, the Takagi-Sugeno-Kang(TSK) fuzzy model is used as a single-view model, and the evidence theory is used for dynamic fusion of multiple views. This method can effectively fuse multiple deep features, and has strong scalability and a certain degree of interpretability. Through verification of the real historical data of multiple FMFs, our model has relatively high accuracy, false detection rate(FDR), and missed detection rate(MDR). © 2024 IEEE.

关键词： Smelting

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Saturation Function-Based Finite-Time Synchronization control for Fractional-Order Coupled Neural Networks

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IEEE Transactions on automation Science and engineering 2025年 22卷 16658-16667页

作者： Zhanshan Wang Bibo Zheng State Key Laboratory of Synthetical Automation for Process Industries and the College of Information Science and Engineering Northeastern University Shenyang China School of Information and Control Engineering China University of Mining and Technology Xuzhou China

In the existing research on finite-time synchronization (FTS) control for fractional-order coupled neural networks (FOCNNs), signum function plays a crucial role in controller design. The discontinuity of the signum function causes the chattering phenomenon to worsen the performance of controlled system. In this paper, a saturation function is utilized instead of signum function in controller design, overcoming the shortcomings of previous control schemes. Due to the introduction of the saturation function, the system exhibits different dynamic behaviors within and outside the boundary of the saturation function. To further analyze this effect, the two-stage fractional-order nonlinear differential inequalities (TFNDIs) are established, which provides an effective tool for handling saturation function-based FTS control for FOCNNs. At last, the validity of proposed theoretical results is demonstrated through numerical simulations, which show that the chattering has been significantly *** to Practitioners—The FTS problems have practical applications in control and engineering, such as multi-robot collaboration, microgrid synchronization and satellite formation flight, etc. However, there is a fact that signum function may cause chattering phenomenon in finite-time control, which makes it difficult to implement in the engineering application of high precision systems. In this paper, a saturation function-based finite-time control scheme has been constructed for FOCNNs to suppress the chattering, but it is difficult to apply the stability method based on integer-order systems directly to fractional-order ones. Therefore, we propose a new TFNDIs method, and give a rigorous proof for FTS. The superiority of proposed method has been demonstrated in the numerical example.

关键词： Synchronization Vectors Stability criteria Couplings Convergence Power system stability Fractional calculus automation Training process control

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A Primal-Dual SGD Algorithm for Distributed Nonconvex Optimization

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IEEE/CAA Journal of Automatica Sinica 2022年第5期9卷 812-833页

作者： Xinlei Yi Shengjun Zhang Tao Yang Tianyou Chai Karl Henrik Johansson Division of Decision and Control Systems School of Electrical Engineering and Computer ScienceKTH Royal Institute of Technologyand also affiliated with the Digital FuturesStockholm 10044Sweden Department of Electrical Engineering University of North TexasDentonTX 76203 USA IEEE State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819China

The distributed nonconvex optimization problem of minimizing a global cost function formed by a sum of n local cost functions by using local information exchange is *** problem is an important component of many machine learning techniques with data parallelism,such as deep learning and federated *** propose a distributed primal-dual stochastic gradient descent(SGD)algorithm,suitable for arbitrarily connected communication networks and any smooth(possibly nonconvex)cost *** show that the proposed algorithm achieves the linear speedup convergence rate O(1/(√nT))for general nonconvex cost functions and the linear speedup convergence rate O(1/(nT)) when the global cost function satisfies the Polyak-Lojasiewicz(P-L)condition,where T is the total number of *** also show that the output of the proposed algorithm with constant parameters linearly converges to a neighborhood of a global *** demonstrate through numerical experiments the efficiency of our algorithm in comparison with the baseline centralized SGD and recently proposed distributed SGD algorithms.

关键词： Distributed nonconvex optimization linear speedup Polyak-Lojasiewicz(P-L)condition primal-dual algorithm stochastic gradient descent

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Queue estimation for isolated signalized intersections in intelligent vehicle-infrastructure cooperation systems

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Science China(Information Sciences) 2021年第4期64卷 226-228页

作者： Yunpeng WANG Ge GUO Wei YUE School of Control Science and Engineering Dalian University of Technology State Key Laboratory of Synthetical Automation for Process Industries Northeastern University School of Control Engineering Northeastern University at Qinhuangdao School of Marine Electrical Engineering Dalian Maritime University

Dear editor,Traffic jams have become an important issue in urban networks, particularly for arterial roads [1]. The need for more efficient traffic control techniques has become critical. One good solution is the so-called intelligent vehicleinfrastructure cooperation systems (i-VICS), which assumes autonomous vehicles in the traffic flow (i.e., a mixed traffic flow). The aim of i-VICS is to achieve cooperative vehicle intersection control, i.e., to enable cooperation between vehicles and traffic signals, for safe and efficient intersection operations.

关键词： Intelligent Vehicle-Infrastructure Cooperation Systems (i-VICS) traffic information prediction real time queue length queue length estimation traffic flow model

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Trend-Aware Patch Feature Fusion Transformer for Fault Diagnosis of Vibration Signals

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IEEE Sensors Journal 2025年

作者： Zhang, Ruicong Chu, Fei Qiu, Yazhi He, Dakuo Wang, Fuli China University of Mining and Technology School of Information and Control Engineering Xuzhou221000 China State Key Laboratory of Intelligent Optimized Manufacturing in Mining & Metallurgy Process Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing100160 China Northeastern University College of Information Science and Engineering State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Northeastern University College of Information Science and Engineering Shenyang110000 China

The Vision Transformer (ViT) has demonstrated reliable performance in industrial equipment fault diagnosis by capturing global features and long-range dependencies in vibration signals. Nonetheless, when processing vibration signals, ViT primarily focuses on the correlations between patches, while neglecting the trend variation information hidden between signal units. This limitation hinders the model’s ability to extract latent fault information. To address this issue, we propose a trend-aware patch feature fusion Transformer (TAPformer) for fault diagnosis of vibration signals. Specifically, we design both patch-level and signal-level trend-aware self-attention blocks and adopt a weighted approach to integrate coarse-grained patch-level trend features with fine-grained signal-level trend features at the output of each self-attention head. This approach enables the model to capture fault information hidden within trend variations, effectively enhancing its fault detection capabilities. TAPformer establishes optimal weighted fusion hyperparameters through ablation experiments and is tested on four public datasets. Compared to eight advanced models, TAPformer achieves the highest accuracy across all four datasets, demonstrating the effectiveness and generalization capability of the proposed method. © 2001-2012 IEEE.

关键词： Fault detection

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Robust PD-type iterative learning control design for uncertain batch processes subject to nonrepetitive disturbances 41

Robust PD-type iterative learning control design for uncerta...

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第41届中国控制会议

作者： Robert Maniarski Wojciech Paszke Shoulin Hao Hongfeng Tao Institute of Automation Electronic and Electrical EngineeringUniversity of Zielona Góra Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Jiangnan University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This paper develops PD-type iterative learning control schemes for a class of uncertain batch processes subject to nonrepetitive *** means of two-dimensional/repetitive setting,the sufficient conditions for batch-to-batch error convergence and H disturbance attenuation are formulated and ***,the procedure for computing the desired control law matrices is formulated in terms of solvability of linear matrix *** proposed control law is able to fulfil the imposed design specifications,i.e.,they are suitable for the batch processes with time-varying uncertainties as well as non-repetitive *** illustrative example is used to validate the proposed control scheme and demonstrates a possible applicability of the developed results.

关键词： iterative learning control repetitive processes batch processes robust control nonrepetitive disturbances

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Breaking Limitation of Convergence in Continuous Optimization: Predefined-Time Gradient Flows 63

Breaking Limitation of Convergence in Continuous Optimizatio...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Zhang, Renyongkang Guo, Ge Gao, Zhenyu Yu, Miao Zhang, Lu Wang, Zhengsong Han, Meng Yin, Yanyan Northeastern University College of Information Science and Engineering Shenyang110819 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Northeastern University at Qinhuangdao School of Control Engineering Qinhuangdao066004 China Curtin University School of Electrical Engineering Computing and Mathematical Sciences PerthWA6102 Australia

ISBN: (纸本)9798350316339

This paper investigates the continuous-time unconstrained optimization via dynamical gradient flow systems. For strongly convex objective functions, a gradient flow with predefined time convergence is proposed, which is suitable for relaxing the objective function to only satisfy the PolyakLojasiewicz (P-L) condition. A predefined-time convergent Newton's flow is designed for the minimization of strictly convex functions. The proposed result is extended for strongly convex objective functions with time-varying parameters. In our schemes, the upper bound of convergence time can be used as a prior choice without involving system initialization and complex parameter calculations. The convergence of algorithms is rigorously proved by using the Lyapunov method. Numerical simulation illustrates the effectiveness of the proposed methods. © 2024 IEEE.

关键词： Lyapunov methods

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Design of remote group control system based on thermal environment of MODBUS launch vehicle 3

Design of remote group control system based on thermal envir...

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3rd International Conference on Intelligent control, Measurement and Signal processing and Intelligent Oil Field, ICMSP 2021

作者： Li, Yijie Li, Hong Li, Pu Cheng, Ying School of Electronic Engineering Xi'An Shiyou University Automation Instrument and Process Control Laboratory Xi an China

ISBN: (纸本)9781665437158

Aiming at the problems of the ground simulation experiment of carrier rocket, such as the large number of regions divided, the different length of the device and the different model curve, the remote group control scheme of the rocket thermal environment was designed and realized. The system takes 108 sets of AC-DC conversion power electronic devices as the main power components, uses Ethernet Modbus TCP/IP communication protocol, and applies configuration software WinCC and S7-200 Smart PLC as the execution terminal. To be realized the monitoring of 108 AC - DC conversion power electronic equipment by a computer via multi-port switch. The system has been successfully applied in a research institute in Beijing, which improves the working efficiency and ensures the reliability and stability of the operation. © 2021 IEEE.

关键词： Rectifying circuits

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Topology-Informed Graph Convolutional Network for Fault Diagnosis 11

Topology-Informed Graph Convolutional Network for Fault Diag...

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11th IEEE Data Driven control and Learning Systems Conference, DDCLS 2022

作者： Jia, Mingwei Liu, Yi Xu, Danya Yang, Tao Yao, Yuan Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou310023 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China National Tsing Hua University Department of Chemical Engineering 30013 Taiwan

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

The development of an accurate data-driven fault diagnosis model faces several obstacles. One significant challenge is how to combine the model with the process mechanism rationally and improve the model interpretability. In this work, a fault diagnosis method based on a topology-informed graph convolutional network (TIGCN) is proposed. This model first uses the empirical knowledge that analyzes the operating state of the process and combines the operating data to construct a signed directed graph. Then the operating data is divided into three modalities in the time dimension. The model utilizes a well-designed spatial-temporal convolutional layer and signed directed graph to construct different modalities separately to effectively detect fault types. The superior fault diagnosis performance of the proposed TIGCN model is demonstrated on the Tennessee-Eastman process as compared to traditional approaches. © 2022 IEEE.

关键词： Failure analysis

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Time-Attention Graph Convolutional Network Soft Sensor in Biochemical processes 4

Time-Attention Graph Convolutional Network Soft Sensor in Bi...

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4th International Conference on Industrial Artificial Intelligence, IAI 2022

作者： Jia, Mingwei Xu, Danya Yang, Tao Yao, Yuan Liu, Yi Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou310023 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China National Tsing Hua University Department of Chemical Engineering 30013 Taiwan

ISBN: (数字)9781665451208

ISBN: (纸本)9781665451208

Most data-driven soft sensor methods can model nonlinear time-varying characteristics of biochemical processes. However, the intrinsic relationship between variables, which is helpful for understanding model behavior, has rarely been investigated in existing data-driven methods. In this work, a novel soft sensor model of time-attention graph convolutional network (TA-GCN) is proposed, which jointly leverages variable relationships and long-term temporal dependencies to improve interpretability and prediction accuracy. This model first uses the maximum information coefficient to construct a topology graph and trains edge strengths end-to-end. The data are then encoded in the spatial-temporal dimension based on GCN and attention mechanism. Finally, the empirical knowledge that analyzes the operating state of the process and graph are combined to explain the model behavior. In comparison to existing soft sensors, TA-GCN enables efficient and scalable training for long-term spatial-temporal dependencies. Experimental results on InPenSim dataset demonstrate that TA-GCN is competitive with state-of-the-art methods. © 2022 IEEE.

关键词： Convolution

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