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Steps Toward Industry 5.0:Building“6S”Parallel Industries With Cyber-Physical-Social Intelligence

IEEE/CAA Journal of automatica Sinica 2023年第8期10卷 1692-1703页

作者： Xingxia Wang Jing Yang Yutong Wang Qinghai Miao Fei-Yue Wang Aijun Zhao Jian-Ling Deng Lingxi Li Xiaoxiang Na Ljubo Vlacic School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China IEEE Macao University of Science and Technology Macao 999078China Beijing Engineering Research Center of Intelligent Systems and Technology Chinese Academy of SciencesBeijing 100098 State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China North Automatic Control Technology Institute Taiyuan 030006China China Huaneng Group Co. Ltd.Beijing 100031China Department of Electrical and Computer Engineering Indiana University-Purdue University Indianapolis(IUPUI)IndianapolisIN 46202 USA Department of Engineering University of CambridgeCambridgeCB21PZUK School of Engineering and Built Environment Griffith UniversityGold CoastQLD 4222Australia

Very recently,intensive discussions and studies on Industry 5.0 have sprung up and caused the attention of researchers,entrepreneurs,and policymakers from various sectors around the ***,there is no consensus on why and what is Industry 5.0 *** this paper,we define Industry 5.0from its philosophical and historical origin and evolution,emphasize its new thinking on virtual-real duality and human-machine interaction,and introduce its new theory and technology based on parallel intelligence(PI),artificial societies,computational experiments,and parallel execution(the ACP method),and cyber-physical-social systems(CPSS).Case studies and applications of Industry 5.0 over the last decade have been briefly summarized and analyzed with suggestions for its future *** believe that Industry 5.0 of virtual-real interactive parallel industries has great potentials and is critical for building smart *** are outlined to ensure a roadmap that would lead to a smooth transition from CPS-based Industry 4.0 to CPSS-based Industry 5.0 for a better world which is Safe in physical spaces,S ecure in cyberspaces,Sustainable in ecology,Sensitive in individual privacy and rights,Service for all,and Smartness of all.

关键词： ACP artificial intelligence CPS CPSS Industry 4.0 Industry 5.0 parallel industries parallel intelligence

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Physics-guided graph learning soft sensor for chemical processes

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Chemometrics and Intelligent laboratory Systems 2024年 249卷

作者： Liu, Yi Jia, Mingwei Xu, Danya Yang, Tao Yao, Yuan Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou310023 China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China Department of Chemical Engineering National Tsing Hua University Hsinchu300044 Taiwan

The surge in data-driven soft sensors for industrial processes is evident. However, most of them suffer from the limitation of being black-box models and this will hamper their widespread use. In response to this challenge, this study proposes a physics-guided graph-learning soft sensor that integrates a physical understanding of industrial processes by incorporating graph-based concepts with process physics. The soft sensor first constructs physical information based on causal relationships between variables using the conditional Granger causality test. Subsequently, it autonomously learns the unique sample information of each observation while employing a regularization loss to ensure the sparsity of the learned information. The model employs a two-stream structure for spatiotemporal encoding of both the physical and sample information. The modeling and prediction results on a penicillin fermentation process indicate that, using the proposed method, the knowledge gained from the data aligns with existing prior knowledge. This approach shows promise in filling the gap between data-driven and physics-based modeling in chemical processes. © 2024 Elsevier B.V.

关键词： Graphic methods

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Robustness of gross primary production estimation from long-term reconstructed solar-induced chlorophyll fluorescence varies with greenness on a global scale

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Journal of Applied Remote Sensing 2025年第1期19卷

作者： Fu, Lijiang Xia, Qian Tang, Hao Chen, Junqing Tan, Jinglu Guo, Ya Jiangnan University Ministry of Education Key Laboratory of Advanced Process Control for Light Industry Wuxi China Changshu Institute of Technology School of Electrical Engineering and Automation Changshu China University of Missouri Department of Chemical and Biomedical Engineering ColumbiaMO United States

Long-term reconstructed solar-induced chlorophyll fluorescence (SIF) derived from raw gridded SIF has been used for the estimation of gross primary production (GPP), but the robustness of the spatial relationship may vary from location to location. We examined the often-used linear relationship between GPP and SIF in terms of R2 values for varied locations globally using three GPP datasets (FLUXCOM, VPM, PML) and three long-term reconstructed monthly SIF datasets (CSIF, SIF005, and RTSIF). The results show that the R2 value is a concave function of vegetation greenness level (NDVI) on an annual or seasonal basis. The average R2 is over 0.8 in areas where the annual average NDVI is in the range of 0.4 to 0.6, whereas the R2 is much lower where the annual average NDVI is less than 0.2 or greater than 0.8. Prediction of GPP or SIF by three methods from five major environmental variables revealed greater uncertainties in GPP and/or SIF at low or high greenness levels as an apparent cause of the low R2. The results offer useful insights into how global GPP may be effectively estimated from multi-satellite measured SIF. © 2025 Society of Photo-Optical Instrumentation Engineers (SPIE).

关键词： Optical correlation

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Transductive Transfer Broad Learning Soft Sensor for Multimode Quality Prediction

Transductive Transfer Broad Learning Soft Sensor for Multimo...

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

作者： Jialiang Zhu Hongying Deng Danya Xu Tao Yang 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

In chemical processes, reliable soft sensors are generally established by enough labeled data. However, in most multimode processes, the collection of sufficient labeled data is difficult due to the high cost and complexity. In this work, transductive transfer broad learning (TTBL) is proposed for multimode quality prediction. By transferring the useful information from the related domain, unlabeled data in the prediction domain is utilized for modeling. First, the data feature is extracted by the feature and enhancement nodes. The similarity information of current and related domain data is captured by the $k$ nearest-neighbor graph. Then, label information in the related domain can be transferred and similar information in the same domain can be retained by the manifold regularization framework. Finally, the output weight can be effectively calculated by the ridge regression algorithm. Experimental results on continuous stirred tank reactor datasets show the superiority of TTBL, compared with several common methods.

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Learning-Based Predictive-Corrector Reentry Guidance for Hypersonic Vehicles

Learning-Based Predictive-Corrector Reentry Guidance for Hyp...

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International Conference on Autonomous Unmanned Systems, ICAUS 2021

作者： Wu, Tiancai Wang, Honglun Liu, Yiheng Yu, Yue Lun, Yuebin School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Science and Technology On Aircraft Control Laboratory Beihang University Beijing100191 China Shenyuan Honors College Beihang University Beijing100191 China Beijing Aerospace Automatic Control Institute Beijing100854 China

ISBN: (纸本)9789811694912

A learning-based predictive-corrector guidance method for hypersonic vehicles with a high lift-to-drag ratio is proposed in this paper. First, based on the quasi equilibrium-glide condition, a traditional predictive-corrector guidance algorithm is employed to address reentry guidance with path constraint. Then, to avoid the ubiquitous phenomenon of large phugoid oscillation, a learning-based altitude rate feedback mechanism is proposed. The feedback gain is scheduled adaptively by a deep reinforcement learning strategy to enhance the adaptability and robustness of the guidance algorithm in different flight environments. Finally, the numerical simulation demonstrates the effectiveness of the proposed algorithm. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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Robust adaptive control of hypersonic flight vehicle with asymmetric AOA constraint

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Science China(Information Sciences) 2020年第11期63卷 195-207页

作者： Yuyan GUO Bin XU Weixin HAN Shuai LI Yueping WANG Yu ZHANG School of Automation Northwestern Polytechnical University Department of Computing The Hong Kong Polytechnic University Flight Automatic Control Research Institute State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang University

This paper investigates the state-constrained controller design of a hypersonic flight vehicle(HFV) based on an asymmetric barrier Lyapunov function(ABLF). The robust adaptive back-stepping controller with integral terms is applied for the HFV longitudinal dynamics. Considering the asymmetric angle of attack(AOA) constraint caused by the unique structure and scramjet, the controller is modified by constructing an ABLF, where the asymmetric constraint on AOA tracking error is introduced. Combined with the constraint on virtual control, the AOA is restricted to a predefined asymmetric interval. The system stability and the AOA constraint are guaranteed via Lyapunov analysis. Simulation results verify that the AOA can be kept in the given asymmetric interval while the altitude reference signal is tracked.

关键词： hypersonic flight vehicle HFV integral back-stepping control adaptive control asymmetric barrier Lyapunov function ABLF angle of attack (AOA) constraint

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Power-Hardware-in-the-Loop Motor Emulator under Four-Quadrant Operation Condition

Power-Hardware-in-the-Loop Motor Emulator under Four-Quadran...

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International Conference on Power Electronics and Motion control (IPEMC)

作者： Gengchen Zhang Hong Guo Jinquan Xu Gaoxiang Wang Li Zhang Wenbo Jin Xiaopeng Zhao Ying Liu School of Automation Science and Electrical Engineering Beihang University Bejing China Southwest China Research Institute of Electronic Equipment Chengdu China Xian Flight Automatic Control Research Institute Xi’an China National Key Laboratory of Science and Technology on Aircraft Control Xi’an China

ISBN: (数字)9798350351330

ISBN: (纸本)9798350351347

In order to solve the problems of long cycle and the difficulty of test, this paper proposes a method of power-in-the-loop motor simulator. Firstly, the topology of the motor simulator is proposed, composed of an interface filtering unit, a motor simulation unit and an energy management unit. Secondly, a discretization method for permanent magnet synchronous motor(PMSM) is designed for DSP control. Thirdly, this paper proposes a method combining quasi-proportional resonance control(QPR) and direct current control to ensure the control accuracy. Finally, the motor simulator is verified by experiments.

关键词： Current control Accuracy Filtering Motors Resonance Permanent magnets Power electronics

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A fuzzy PID method for suppressing drilling stick-slip vibration by introducing a Smith predictor

A fuzzy PID method for suppressing drilling stick-slip vibra...

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2021 CAA Symposium on Fault Detection, Supervision, and Safety for Technical processes, SAFEprocess 2021

作者： Guangwu, Chen Dejun, Ba Xin, Zhou Peng, Li Lanzhou Jiaotong University School of Automation and Electrical Engineering Lanzhou China Institute of Automatic Control Lanzhou Jiaotong University Gansu Provincial Key Laboratory of Highland Traffic Information Engineering and Control Lanzhou China

ISBN: (纸本)9781665401159

During the drilling process of an oil rig, the torsional stiffness decrease as the drilling depth increase and it leads to a stick-slip vibration of the drill string system. The dynamic process of torsional stiffness and moment of inertia is complex while it is hard to control the influence of model parameters. In this paper, the mechanism of vibration that meets the actual situation is analyzed, and a new method that uses fuzzy PID control with the Smith predictor is proposed. The simulation and results show that the proposed method can suppress the hysteresis effect and has a certain inhibitory effect on the occurrence of stick-slip vibration. © 2021 IEEE.

关键词： Vibration analysis

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Design of indirect-type iterative learning control for continuous-time batch processes with the repetitive process setting

Design of indirect-type iterative learning control for conti...

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Asian control Conference

作者： Robert Maniarski Wojciech Paszke Hongfeng Tao Zhihe Zhuang Institute of Automation Electronic and Electrical Engineering University of Zielona Zielona Góra Poland Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Jiangnan University Wuxi China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

An indirect iterative learning control law is proposed for a class of continuous-time batch processes with time-varying uncertainties, input delay, and disturbances. First, by designing a predictor based on a state observer we are able to estimate the future state and compensate for the input delay. Then, based on the estimated state we design a robust $H$ ∞ controller in the presence of time-varying uncertainties and load disturbances. Additionally, the differential repetitive process setting for the design of the iterative learning control scheme is applied and hence the convergence of the tracking error in trial-to-trial direction occurs. Finally, the simulation results verify the new method's effectiveness.

关键词： Uncertainty Simulation Batch production systems process control Observers Delays Iterative learning control

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Modeling, Analysis and Simulation of the Effect of Temperature on Magnetostrictive Actuators 9

Modeling, Analysis and Simulation of the Effect of Temperatu...

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9th International Conference on Fluid Power and Mechatronics, FPM 2023

作者： Zhang, Xinyuan Wang, Shaoping Niu, Yu Wu, Xiao Wang, Xingjian Gavrilov, Alex I. School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Tianmushan Laboratory Hangzhou310023 China Ningbo Institute of Technology Beihang University Ningbo315800 China Research Institute of Aero-Engine Beihang University Beijing100191 China Bauman Moscow State Technical University Department of Automatic Control Systems Moscow105005 Russia

ISBN: (纸本)9798350304947

The increased demand for active control of engines has made the study of high-frequency response actuators increasingly important, and actuators based on magnetostrictive materials are promising for a wide range of applications. In this paper, the magnetic field distribution and various thermal losses including Joule heat loss, eddy current loss and hysteresis loss inside the giant magnetostrictive actuator (GMA) are analyzed, and the mathematical model of the current-driven GMA operation is derived, where the new J-A model considering the temperature effect is used to describe the hysteresis nonlinearity of the material. This paper simulates the actuator model under the action of multiple physical fields in COMSOL, focusing on the variation of the output displacement of the actuator when the thermal field and thermal expansion are integrated, and provides the theoretical basis for the design of the GMA control algorithm in the future. © 2023 IEEE.

关键词： Temperature

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