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Chinese automation Congress (CAC)

作者： Chao Xi Gaochang Wu Zizhu Fan Feng Xu Hui Wang Xiaokun Tang 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: (数字)9798350368604

ISBN: (纸本)9798350368611

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).

关键词： Fuses Heuristic algorithms Furnaces Smelting Production Feature extraction Transformers Magnesium Classification algorithms Unsupervised learning

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LQG Control Over SWIPT-enabled Wireless Communication Network

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IFAC-PapersOnLine 2023年第2期56卷 3824-3829页

作者： Huiwen Yang Lingying Huang Yuzhe Li Subhrakanti Dey Ling Shi Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong China School of Electrical and Electronic Engineering Nanyang Technological University Singapore State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang 110004 China Department of Electrical Engineering Uppsala University Uppsala Sweden

In this paper, we consider using simultaneous wireless information and power transfer (SWIPT) to recharge the sensor in the LQG control, which provides a new approach to prolonging the network lifetime. We analyze the stability of the proposed system model and show that there exist two critical values for the power splitting ratio α. Then, we propose an optimization problem to derive the optimal value of α. This problem is non-convex but its numerical solution can be derived by our proposed algorithm efficiently. Moreover, we provide the feasible condition of the proposed optimization problem. Finally, simulation results are presented to verify and illustrate the main theoretical results.

关键词： Networked control systems SWIPT packet drop stability

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A Fault Diagnosis Method Based on Canonical Correlation Analysis and Transfer Learning in Wastewater Treatment Plant 37

A Fault Diagnosis Method Based on Canonical Correlation Anal...

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37th Youth Academic Annual Conference of Chinese Association of automation, YAC 2022

作者： Chen, Xin Li, Linlin Peng, Xin Yang, Dan University of Science and Technology Beijing School of Automation and Electrical Engineering Beijing China Shunde Graduate School University of Science and Technology Beijing Foshan China Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai China

ISBN: (纸本)9781665465366

Methods of multivariate statistics have gained remarkable popularity in the last few decades. However, the lack of data in the target domain often limits the performance of fault diagnosis in industrial processes. To solve this problem, this paper proposes a fault diagnosis approach based on canonical correlation analysis and transfer learning. Different from the standard method, this method has the following characteristics. Firstly, based on the canonical correlation analysis method, the diagnosis model of source domain can be obtained using a large amount of source domain data. Secondly, the transfer learning method based on the local retention canonical correlation analysis of K nearest neighbors transfers the fault diagnosis model of the source domain to the target domain, which alleviates the problem of insufficient data in the target domain through migration. Thirdly, a process monitoring statistic based on the Riemannian distance is proposed. Finally, the data from the wastewater treatment plant are analyzed to demonstrate the effectiveness of the proposed method. © 2022 IEEE.

关键词： Fault detection

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Composite Nonlinear Trajectory Tracking Control of Co-Driving Vehicles Using Self-Triggered Adaptive Dynamic Programming

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IEEE Transactions on Consumer Electronics 2025年

作者： Hu, Chuan Ge, Sicheng Shi, Yingkui Gao, Weinan Guo, Wenfeng Zhang, Xi Shanghai Jiao Tong University Intelligent Vehicle Institute School of Mechanical Engineering Shanghai200240 China Shanghai Jiao Tong University School of Mechanical Engineering Shanghai200240 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Tsinghua University School of Vehicle and Mobility Beijing100190 China

This article presents a composite nonlinear feedback (CNF) control method using self-triggered (ST) adaptive dynamic programming (ADP) algorithm in a human-machine shared steering framework. For the overall system dynamics, a two-degrees-of-freedom (2-DOF) vehicle model is established and a two-point preview driver model is adopted. A dynamic authority allocation strategy based on cooperation level is proposed to combine the steering input of the human driver and the automatic controller. To make further improvements in the controller design, three main contributions are put forward. Firstly, the CNF controller is designed for trajectory tracking control with refined transient performance. Besides, the self-triggered rule is applied such that the system will update in discrete times to save computing resources and increase efficiency. Moreover, by introducing the data-based ADP algorithm, the optimal control problem can be solved through iteration using system input and output information, reducing the need for accurate knowledge of system dynamics. The effectiveness of the proposed control method is validated through Carsim-Simulink co-simulations in diverse driving scenarios. © 2025 IEEE. All rights reserved.

关键词： Steering

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Q-Learing-Based Multi-Rate Optimal Control and Its Application

Q-Learing-Based Multi-Rate Optimal Control and Its Applicati...

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第34届中国控制与决策会议

作者： Zhenxing Xia Wei Dai School of Information and Control Engineering China University of Mining and Technology State Key Laboratory of Synthetical Automation for Process Industries Northeastern University

In this paper,adaptive dynamic programming(ADP)algorithm,with lifting technology,is develop to solve the multi-rate optimal control problem for discrete-time linear *** make use of the lifting technology to convert the multi-rate sample control problem to the single-rate one in the uniform *** propose a Q-Learning based approach to learn the optimal regulator by a value iteration(Ⅵ) ***,a class of continuous-time(CT) linear system with multi-timescale is ***,the convergence of a Q-Learning based algorithm is *** is proven that the iterative cost function precisely converges to the optimal value,and the control input also converges to the optimal ***,HIL system for grinding process is given to illustrate the effective performance of the proposed method.

关键词： Reinforcement learning(RL) Multi-Rate Optimal Control Lifting Technology

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High-sensitivity Mach–Zehnder interferometer temperature sensor based on a no-core optical fiber with a large lateral offset

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Applied Optics 2025年第16期64卷 4639-4645页

作者： Xinghui Li Huifang Wang Xiaopeng Chen Qi Zhang Xiaoyu Chen Xue Zhou Xin Yan Yong Zhao Tonglei Cheng State Key Laboratory of Synthetical Automation for Process Industries the College of Information Science and Engineering and the National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang 110819 China Foshan Graduate School of Innovation Northeastern University Foshan Guangdong 528311 China Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao 066004 China

We designed and experimentally demonstrated an innovative temperature sensor based on Mach–Zehnder interference, which was formed by splicing a piece of no-core fiber (NCF) between two conventional single-mode fibers (SMFs) with a core offset. The intentional misalignment formed a gap that can be filled with ethanol, and temperature sensing was realized using the mode interference between the two distinct optical paths passing through the NCF cladding and the ethanol solution. Although the NCF length barely influenced the sensor sensitivity, it produced a major impact on the free spectral range (FSR) value. As a result, simulations were conducted and an NCF length of 620.7 µm was prepared. The sensor achieved a remarkable temperature sensitivity of 5.3574 nm/°C within the 30–36 °C range. Furthermore, the sensor boasted multiple advantages, including compact structure, high repeatability, and exceptional stability, rendering it a highly effective and versatile tool for precise thermal measurement in cell culture.

关键词： Atomic layer deposition Beam propagation methods Fiber Bragg grating sensors Fiber optic sensors Position sensors Temperature sensors

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Simulation Based Robustness Evaluation of Train Rescheduling Plan Under Uncertainties

Simulation Based Robustness Evaluation of Train Rescheduling...

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International Conference on automation and Computing (ICAC)

作者： Dezhao Zhao Dongliang Cui Pengxin Yang Ruiguang Liu Xuewu Dai The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China Department of Automotive Engineering Hebei Petroleum University of Technology Chengde China Faculty of Engineering and Environment Northumbria University Newcastle United Kingdom

When a train deviates from its planned operational schedule due to unexpected events causing delays, it becomes necessary to reschedule the train in order to obtain a revised operation plan and minimize the delays. However, evaluating the robustness of the actual train schedule under various uncertainties is a critical aspect of train rescheduling. This paper presents the development of a simulation system for high-speed railway operation scheduling and proposes a method to evaluate the robustness of train rescheduling plans considering uncertain disturbances in the simulation process. The simulation system incorporates a train operation model based on both mechanisms and data, as well as an uncertain interference model for train delays based on data. By introducing various disturbances generated by the uncertain interference model, the robustness of the train rescheduling plan can be simulated and evaluated. Using the Jinan West-Shanghai Hongqiao high-speed railway network as a case study, the rescheduling plans generated by two scheduling algorithms are simulated. The results validate the reliability of the developed simulation system for verifying and evaluating train rescheduling plans. Furthermore, it provides valuable data and technical support for intelligent scheduling of high-speed trains and performance verification of scheduling algorithms.

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A novel strategy to solve communication constraints for formation control of multi-AUVs

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Science China(Information Sciences) 2021年第7期64卷 253-255页

作者： Zhenyu GAO Ge GUO Department of Automation Dalian Maritime University School of Control Engineering Northeastern University Qinhuangdao Key Laboratory of Synthetical Automation for Process Industries Northeastern University

Dear editor,Interest in terms of research on formation control of multiple autonomous underwater vehicles (multi-AUVs) has gained considerable attention in recent years [1–5], owing to its extensive applications in d... 详细信息

关键词： Information Systems and Communication Service

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Data-Based Efficient Off-Policy Stabilizing Optimal Control Algorithms for Discrete-Time Linear Systems via Damping Coefficients

arXiv

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arXiv 2024年

作者： Li, Dongdong Dong, Jiuxiang College of Information Science and Engineering Northeastern University Shenyang110819 China State Key Laboratory of Synthetical Automation of Process Industries Northeastern University Shenyang110819 China Key Laboratory of Vibration and Control of Aero-Propulsion Systems Ministry of Education Northeastern University Shenyang110819 China

Policy iteration is one of the classical frameworks of reinforcement learning, which requires a known initial stabilizing control. However, finding the initial stabilizing control depends on the known system model. To relax this requirement and achieve model-free optimal control, in this paper, two different reinforcement learning algorithms based on policy iteration and variable damping coefficients are designed for unknown discrete-time linear systems. First, a stable artificial system is designed, and this system is gradually iterated to the original system by varying the damping coefficients. This allows the initial stabilizing control to be obtained in a finite number of iteration steps. Then, an off-policy iteration algorithm and an off-policy Q-learning algorithm are designed to select the appropriate damping coefficients and realize data-driven. In these two algorithms, the current estimates of optimal control gain are not applied to the system to re-collect data. Moreover, they are characterized by the fast convergence of the traditional policy iteration. Finally, the proposed algorithms are validated by simulation. Copyright © 2024, The Authors. All rights reserved.

关键词： Damping

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Nonlinear industrial process fault diagnosis with latent label consistency and sparse Gaussian feature learning

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Journal of Central South University 2022年第12期29卷 3956-3973页

作者： LI Xian-ling ZHANG Jian-feng ZHAO Chun-hui DING Jin-liang SUN You-xian Science and Technology on Thermal Energy and Power Laboratory Wuhan 2nd Ship Design and Research InstituteWuhan 430205China State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhou 310027China State Key Laboratory of Synthetical Automation for Process Industries(Northeastern University) Shenyang 110819China

With the increasing complexity of industrial processes, the high-dimensional industrial data exhibit a strong nonlinearity, bringing considerable challenges to the fault diagnosis of industrial processes. To efficiently extract deep meaningful features that are crucial for fault diagnosis, a sparse Gaussian feature extractor(SGFE) is designed to learn a nonlinear mapping that projects the raw data into the feature space with the fault label dimension. The feature space is described by the one-hot encoding of the fault category label as an orthogonal basis. In this way, the deep sparse Gaussian features related to fault categories can be gradually learned from the raw data by SGFE. In the feature space,the sparse Gaussian(SG) loss function is designed to constrain the distribution of features to multiple sparse multivariate Gaussian distributions. The sparse Gaussian features are linearly separable in the feature space, which is conducive to improving the accuracy of the downstream fault classification task. The feasibility and practical utility of the proposed SGFE are verified by the handwritten digits MNIST benchmark and Tennessee-Eastman(TE) benchmark process,respectively.

关键词： nonlinear fault diagnosis multiple multivariate Gaussian distributions sparse Gaussian feature learning Gaussian feature extractor

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