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Fault Detection, Supervision and Safety for Technical processes (SAFEprocess), CAA Symposium on

作者： Qiqi Niu Chengkai Shen Yuting Lyu Yi Liu Le Zhou School of Automation and Electrical Engineering Zhejiang University of Science & Technology Hangzhou Zhejiang China Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou Zhejiang China

ISBN: (纸本)9781665401166

With an increasing application of the multivariate statistical process monitoring method, the fault identification becomes more and more crucial after the fault detection, which is benefited to fault troubleshooting effectively and quickly. To deal with the dynamic process and the impact of random noise on fault identification, a novel fault identification method based on supervised autoregressive dynamic latent variable is proposed in this paper. After the fault label structure is added on the unsupervised model, the relationship between fault information and dynamic latent feature is strengthened. Then, the fault cause is determined based on the fault relevance method. Finally, the validity of method is verified by a numerical example and an actual wastewater treatment case.

关键词： Fault diagnosis process monitoring Bridges Correlation Fault detection Numerical models Safety

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Repetitive process based design of PD-type iterative learning control laws for batch processes with time-delays

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IFAC-PapersOnLine 2023年第2期56卷 1901-1906页

作者： Robert Maniarski Wojciech Paszke Eric Rogers Hongfeng Tao Institute of Automation Electronic and Electrical Engineering University of Zielona Góra ul. Szafrana 2 65-516 Zielona Góra Poland School of Electronics and Computer Science University of Southampton Southampton SO17 1BJ UK Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Jiangnan University Wuxi 214122 P. R. China

This paper uses linear matrix inequality techniques and the Kalman-Yakubovich-Popov lemma to design an iterative learning control law for a class of batch processes with state delays. The design procedure is based on the stability theory for repetitive processes, a class of 2D systems. A numerical example illustrates the new design and demonstrates that the design has advantages compared to the existing alternatives.

关键词： Iterative learning control repetitive processes batch processes linear matrix inequalities systems with delays

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Operating Performance Assessment of Complex Industrial process Based on Kernel Locally Linear Embedding PLS

Operating Performance Assessment of Complex Industrial Proce...

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第32届中国过程控制会议(CPCC2021)

作者： Chu Fei Mo Shuangshuang Wang Fuli Lu Ningyun Ma Xiaoping Research Center of Underground Space Intelligent Control Engineering of the Ministry of Education China University of Mining and Technology School of Information and Control Engineering China University of Mining and Technology State Key Laboratory of Automatic Control Technology for Mining and Metallurgy Process Beijing General Research Institute of Mining & Metallurgy State Key Laboratory of Integrated Automation for Process Industries Northeastern University College of Automation Engineering Nanjing University of Aeronautics and Astronautics

Due to the process disturbances and uncertainties,the operating performance of industrial process may deviate from the optimal operating point along with ***,non-optimal operation may lead to decline production quality and economic *** it is significant to implement online assessment strategy and further analyze the actual process operating performance,so as to make the production process in the best operating performance as far as *** process data is strongly nonlinear due to the change of system state caused by the change of machine performance or operation *** this time,the relationship between the local nearest neighbor samples is ***,the existing methods of operating performance assessment for solving nonlinear problems only focus on the global structure of data,i.e.,the variance,while ignoring the local neighborhood structure in each performance ***,a new method called kernel locally linear embedding PLS(KLLEPLS) is developed and applied for operating performance *** includes offline modeling,online evaluation and non-optimal cause *** algorithm not only has concise expression and similar analytical solutions for PLS,but also retains the original geometric structure of strongly nonlinear data when extracting the maximum correlation information between process ***,the practice and effectiveness of the proposed method are demonstrated by the dense medium coal preparation process and Tennessee Eastman(TE) process.

关键词： Complex industrial process operating performance assessment kernel locally linear embedding PLS non-optimal cause identification

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Nonlinear Cooperative Output Regulation with Input Delay Compensation

arXiv

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arXiv 2024年

作者： Zheng, Shiqi Ahn, Choon Ki Jiang, Xiaowei Yan, Huaicheng Shi, Peng The School of Automation China University of Geosciences Wuhan430079 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China The Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China The Shaoyang Institute of Advanced Manufacturing Technology Shaoyang422000 China The School of Electrical Engineering Korea University Seoul136-701 Korea Republic of The Key Laboratory of Smart Manufacturing in Energy Chemical Process of Ministry of Education East China University of Science and Technology Shanghai200237 China The School of Electrical and Mechanical Engineering The University of Adelaide AdelaideSA5005 Australia

This paper investigates the cooperative output regulation (COR) of nonlinear multi-agent systems (MASs) with long input delay based on periodic event-triggered mechanism. Compared with other mechanisms, periodic event-triggered control can automatically guarantee a Zeno-free behavior and avoid the continuous monitoring of triggered conditions. First, a new periodic event-triggered distributed observer, which is based on the fully asynchronous communication data, is proposed to estimate the leader information. Second, a new distributed predictor feedback control method is proposed for the considered nonlinear MASs with input delay. By coordinate transformation, the MASs are mapped into new coupled ODE-PDE target systems with some disturbance-like terms. Then, we show that the COR problem is solvable. At last, to further save the communication resource, a periodic event-triggered mechanism is considered in the sensor-to-controller transmission in every agent. A new periodic event-triggered filter is proposed to deal with the periodic event-triggered feedback data. The MASs with input delay are mapped into coupled ODE-PDE target systems with sampled data information. Then, Lyapunov-Krasovskii functions are constructed to demonstrate the exponential stability of the MASs. Simulations verify the validity of the proposed results. © 2024, CC BY-NC-ND.

关键词： Feedback control

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Improved LMI-based conditions for designing of PD-type ILC laws for linear batch processes over two-dimensional setting

Improved LMI-based conditions for designing of PD-type ILC l...

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International Conference on control, automation, Robotics and Vision (ICARCV)

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

ISBN: (纸本)9781665476881

This paper considers the problem of designing of iterative learning control (ILC) laws for linear batch processes. Unlike the majority of existing results about ILC law design for linear batch processes over repetitive/two-dimensional setting where Lyapunov theory is applied, this study is focused on formulating the ILC law design procedures by transforming it into an equivalent problem of (structural) stability analysis for a linear Roesser model for two-dimensional (2D) systems. Then, based on a non-conservative version of stability and stabilization conditions for linear 2D systems, suitable PD-type ILC laws are derived by the application of the linear matrix inequality (LMI) approach. Finally, a numerical example is given to show the validity of the proposed design procedure and some advantages are emphasized when compared to the existing alternatives.

关键词： State feedback Uncertainty Two dimensional displays Batch production systems Stability analysis Structural engineering Numerical models

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Deep Graph Network for process Soft Sensor Development

Deep Graph Network for Process Soft Sensor Development

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International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS)

作者： Mingwei Jia Yun Dai Danya Xu Tao Yang Yuan Yao Yi Liu Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China National Tsing Hua University Hsinchu Taiwan

ISBN: (纸本)9781665402460

In the (bio)chemical processes, traditional hardware sensors are difficult to directly measure the quality of critical products due to their time-varying, non-linear, and dynamic characteristics. This makes process soft sensor modeling methods important. Since the process variables can be regarded as natural graph data, this work introduces graphs in the soft sensor modeling area. A soft sensor model based on the graph neural network (GNN) is proposed. The model can learn the topological structure of graph data between each unit variable. Moreover, it characterizes variable relationships from the spatial and temporal dimensions to the output prediction by introducing the spatial-temporal convolutional layer. The effectiveness and advantages of the GNN-based soft sensor model are verified using a simulated fermentation process.

关键词： Soft sensors Computational modeling Predictive models Sensor phenomena and characterization Data models Product design Hardware

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Enhanced Soft Sensor with Qualified Augmented Data Using Centroid Measurement Criterion

Enhanced Soft Sensor with Qualified Augmented Data Using Cen...

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International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS)

作者： Yun Dai Qing Yu Tao Yang Yuan Yao Yi Liu Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China National Tsing Hua University Hsinchu Taiwan

ISBN: (纸本)9781665402460

Development of reliable soft sensors using limited labeled samples is not an easy task in industrial processes. A selective generative adversarial network (SGAN)-based support vector regression (SGAN-SVR) soft sensor is proposed for quality prediction using limited labeled training data. Specifically, SVR is considered as a base prediction model. The Wasserstein GAN (WGAN) is adopted to capture the distribution of available labeled data and generate virtual candidates. Subsequently, using a proposed similarity measurement strategy, those synthetic data with more information are selected and introduced into the training set. Using the designed data augmentation approach, the SGAN-SVR model can achieve better prediction performance compared with the SVR soft sensor. The quality prediction results on an industrial polyethylene process demonstrate the effectiveness and advantages of the proposed method.

关键词： Training Support vector machines Polyethylene Filtering Soft sensors Training data Predictive models

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Operating Performance Assessment of Plant-Wide processes Based on Hierarchical Multiblock Performance-Relevant Kernel Independent Component Analysis

Operating Performance Assessment of Plant-Wide Processes Bas...

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第40届中国控制会议

作者： Yan Liu Fei Chu Fuli Wang Dakuo He College of Information Science&Engineering Northeastern University College of Information and Control Engineering China University of Mining and Technology State Key Laboratory of Synthetical Automation for Process Industries Northeastern University

The operating performance assessment of plant-wide processes is very important for improving product quality and comprehensive economic *** this study,a new operating performance assessment method based on hierarchical multiblock performance-relevant kernel independent component analysis(HMPRKICA) is proposed for nonlinear and non-Gaussian plant-wide *** proposed approach divides the process variables into several sub-processes according to the physical structure and incorporates sub-process information into high level for operating performance assessment of plant-wide *** the new method,the difficulty of process analysis is reduced and the interpretability of the assessment results are *** process operating performance is nonoptimal,the responsible sub-process is first located by the sub-process assessment result,and then the variable contribution rates are calculated in the corresponding sub-process to narrow the search for the nonoptimal ***,a case study of gold hydrometallurgy process evaluates the efficiency of the proposed method.

关键词： Plant-wide process operating performance assessment HMPRKICA non-Gaussianity nonlinearity

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Secure Synchronization control for a Class of Cyber-Physical Systems With Unknown Dynamics

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IEEE/CAA Journal of Automatica Sinica 2020年第5期7卷 1215-1224页

作者： Ning Wang Xiaojian Li College of Information Science and Engineering Northeastern UniversityShenyang 110819China State Key Laboratory of Synthetical Automation for Process Industries Northeastern Universityand with the Key Laboratory of Vibration and Control of Aero-Propulsion SystemMinistry of EducationNortheastern Universityand also with the College of Information Science and EngineeringNortheastern UniversityShenyang 110819China IEEE

This paper investigates the secure synchronization control problem for a class of cyber-physical systems(CPSs)with unknown system matrices and intermittent denial-of-service(DoS)*** the attack free case,an optimal control law consisting of a feedback control and a compensated feedforward control is proposed to achieve the synchronization,and the feedback control gain matrix is learned by iteratively solving an algebraic Riccati equation(ARE).For considering the attack cases,it is difficult to perform the stability analysis of the synchronization errors by using the existing Lyapunov function method due to the presence of unknown system *** order to overcome this difficulty,a matrix polynomial replacement method is given and it is shown that,the proposed optimal control law can still guarantee the asymptotical convergence of synchronization errors if two inequality conditions related with the DoS attacks ***,two examples are given to illustrate the effectiveness of the proposed approaches.

关键词： Algebraic Riccati equation(ARE) complex dynamical networks(CDNs) denial-of-service(DoS) secure control

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Leveraging Multi-stream Information Fusion for Trajectory Prediction in Low-illumination Scenarios: A Multi-channel Graph Convolutional Approach

arXiv

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arXiv 2022年

作者： Gong, Hailong Li, Zirui Lu, Chao Du, Guodong Gong, Jianwei School of Mechanical Engineering Beijing Institute of Technology Beijing100081 China Chair of Traffic Process Automation "Friedrich List" Faculty of Transport and Traffic Sciences TU Dresden Germany Institute of Dynamic System and Control ETH Zurich Sonneggstrasse 3 Zurich8092 Switzerland

Trajectory prediction is a fundamental problem and challenge for autonomous vehicles. Early works mainly focused on designing complicated architectures for deep-learning-based prediction models in normal-illumination environments, which fail in dealing with low-light conditions. This paper proposes a novel approach for trajectory prediction in low-illumination scenarios by leveraging multi-stream information fusion, which flexibly integrates image, optical flow, and object trajectory information. The image channel employs Convolutional Neural Network (CNN) and Long Short-term Memory (LSTM) networks to extract temporal information from the camera. The optical flow channel is applied to capture the pattern of relative motion between adjacent camera frames and modelled by Spatial-Temporal Graph Convolutional Network (ST-GCN). The trajectory channel is used to recognize high-level interactions between vehicles. Finally, information from all the three channels is effectively fused in the prediction module to generate future trajectories of surrounding vehicles in low-illumination conditions. The proposed multi-channel graph convolutional approach is validated on HEV-I and newly generated Dark-HEV-I, egocentric vision datasets that primarily focus on urban intersection scenarios. The results demonstrate that our method outperforms the baselines, in standard and low-illumination scenarios. Additionally, our approach is generic and applicable to scenarios with different types of perception data. The source code of the proposed approach is available at https://***/TommyGong08/MSIF. Copyright © 2022, The Authors. All rights reserved.

关键词： Optical flows

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