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IEEE Transactions on Industrial Cyber-Physical systems 2023年 1卷 227-235页

作者： Le Zhou Lixinqian Yu Beiping Hou Hongbo Zheng Zheng-Guang Wu School of Automation and Electrical Engineering Zhejiang University of Science & Technology Hangzhou China Faculty of Engineering and IT University of Melbourne Melbourne Australia National Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China

Although remarkable studies on dynamic process modeling and monitoring in cyber-physical systems have been conducted, there are still several limitations to these approaches, rendering them unsuitable for practical applications. Firstly, the common dynamic models are built based on complete data, which means that their modeling performance usually deteriorates when there are missing measurements. Secondly, practical data often exhibits more complex characteristics, such as non-stationary and non-Gaussian noises. Therefore, a robust dynamic latent variable model is proposed to address the challenges of dynamic process modeling and monitoring in complex measurement environments with missing values. The proposed method effectively handles non-Gaussian noises and recursively estimates missing values, thus enhancing the monitoring performance within the dynamic latent variable modeling framework. Finally, the feasibility and superiority of the proposed method are evaluated through a numerical example and a real wastewater treatment case, demonstrating its effectiveness and practicality in real-world scenarios.

关键词： Cyber-physical systems Principal component analysis Process monitoring Data models Load modeling Numerical models Loading

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Exploring the use of deep learning in task-flexible ILC*

Exploring the use of deep learning in task-flexible ILC*

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American control Conference (ACC)

作者： Anantha Sai Hariharan Vinjarapu Yorick Broens Hans Butler Roland Tóth Department of Electrical Engineering Eindhoven University of Technology Eindhoven The Netherlands ASML Veldhoven The Netherlands Systems and Control Laboratory Institute for Computer Science and Control Hungary

Growing demands in today’s industry results in increasingly stringent performance and throughput specifications. For accurate positioning of high-precision motion systems, feedforward control plays a crucial role. Nonetheless, conventional model-based feedforward approaches are no longer sufficient to satisfy the challenging performance requirements. An attractive method for systems with repetitive motion tasks is iterative learning control (ILC) due to its superior performance. However, for systems with non-repetitive motion tasks, ILC is generally not applicable, despite of some recent promising advances. In this paper, we aim to explore the use of deep learning to address the task flexibility constraint of ILC. For this purpose, a novel Task Analogy based Imitation Learning (TAIL)-ILC approach is developed. To benchmark the performance of the proposed approach, a simulation study is presented which compares the TAIL-ILC to classical model-based feedforward strategies and existing learning-based approaches, such as neural network based feedforward learning.

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Autonomous Crowdsensing: Operating and Organizing Crowdsensing for Sensing automation

arXiv

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arXiv 2024年

作者： Wu, Wansen Yang, Weiyi Li, Juanjuan Zhao, Yong Zhu, Zhengqiu Chen, Bin Qiu, Sihang Peng, Yong Wang, Fei-Yue College of Systems Engineering National University of Defense Technology Hunan Province Changsha410073 China Hunan Institute of Advanced Technology Hunan Province Changsha410073 China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

—The precise characterization and modeling of Cyber-Physical-Social systems (CPSS) requires more comprehensive and accurate data, which imposes heightened demands on intelligent sensing capabilities. To address this issue, Crowdsensing Intelligence (CSI) has been proposed to collect data from CPSS by harnessing the collective intelligence of a diverse workforce. Our first and second Distributed/Decentralized Hybrid Workshop on Crowdsensing Intelligence (DHW-CSI) have focused on principles and high-level processes of organizing and operating CSI, as well as the participants, methods, and stages involved in CSI. This letter reports the outcomes of the latest DHW-CSI, focusing on Autonomous Crowdsensing (ACS) enabled by a range of technologies such as decentralized autonomous organizations and operations, large language models, and human-oriented operating systems. Specifically, we explain what ACS is and explore its distinctive features in comparison to traditional crowdsensing. Moreover, we present the "6A-goal" of ACS and propose potential avenues for future research. Copyright © 2024, The Authors. All rights reserved.

关键词： Cyber Physical System

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Complex Fusion Structure Slow Dissociation Simulation Prediction Method

Complex Fusion Structure Slow Dissociation Simulation Predic...

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2024 China automation Congress, CAC 2024

作者： Zhang, Dongsheng Wang, Yigeng Huang, Wanyi Naval Architecture & Ocean Engineering Institute China Shipbuilding NDRI Engineering Co.Ltd Shanghai200090 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China School and Hospital of Stomatology China Medical University Shenyang110016 China Liaoning Provincial Key Laboratory of Oral Diseases China Medical University Shenyang110016 China

ISBN: (纸本)9798350368604

Human skeletal implants, as a category of critically important medical consumables, are designed to remain inside the body for long periods. Titanium or titanium alloys are commonly chosen as the primary construction materials. Recently, the academic community has explored an innovative approach to implant construction that uses titanium as the base framework and zinc as the filling material to create an interconnected composite structure. In this structure, zinc gradually dissolves over the lifetime of the implant, promoting bone growth into the pores formed after its dissolution. Given the continuous dissociation of zinc, the physical and chemical properties of the implant exhibit a slow and continuous change over its lifecycle. However, there is currently no effective method to precisely predict and assess the state changes of such complex structures over long periods of *** study focuses on the typical application scenarios of human skeletal implants, and verifies the accuracy of short-term steady-state numerical calculation models through rigorous experimental methods. Based on this, we propose a complex fusion structure slow dissociation reverse simulation prediction method based on short-term steady-state calculation results. This method aims to simulate the steady-state performance of implants in the short term, and reverse deduce the structural changes and performance evolution caused by zinc dissociation during long-term use, thereby providing a more scientific basis and support for the design and evaluation of medical implants. © 2024 IEEE.

关键词： Titanium alloys

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Optimizing the Structural Parameters of the Mechanical Sensing Element of a Microoptoelectromechanical Micro-g Accelerometer

Optimizing the Structural Parameters of the Mechanical Sensi...

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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Evgenij S. Barbin Tamara G. Nesterenko Aleksei N. Koleda Evgeniy V. Shesterikov Ivan V. Kulinich Andrey A. Kokolov Laboratory of Radiophotonics The Tomsk State University of Control Systems and Radioelectronics. Laboratory of Intelligent Computer Systems Institute of Optics and Atmosphere Siberian Branch of the Russian Academy of Sciences Tomsk Russia Laboratory of Radiophotonics The Tomsk State University of Control Systems and Radioelectronics Tomsk Russia

The paper investigates a microoptoelectromechanical accelerometer that implements optical measuring transducer built on the optical tunneling effect. The article discusses the issues of micromechanical sensing element (MSE) optimization for an MOEM- accelerometer fabricated by silicon on insulator (SOI) technology. The results represent the requirements to the design to achieve a sensitivity of 1 μg m/s 2 at a calculated eigenvalue of the MSE. The main parameters of the mechanical part of a MOEM-accelerometer affecting its sensitivity are dimensions, mass, resonance frequency and corresponding stiffness of the spring suspension. Following the Brownian noise that characterizes the accelerometer's sensitivity, the relations between the parameters are determined. Two designs of the MSE are considered according to the fabrication process. One design contains only a device layer; the second one implements a SOI-wafer basic layer as a weighting additive. The latter design occupies less area. To prevent MSE deflection stipulated by its larger dimensions and to increase the number of sensors fabricated from a single SOI wafer, MSE with additional mass are more reasonable to use. The MSE mass at an eigenfrequency of 10, 20, 50 and 100 Hz should exceed 0.01.10 −6 kg, while at a frequency of 200 Hz, the mass should exceed 0.023.10 −6 kg to comply with the requirement of minimal measurable acceleration of 1 μg m/s 2 . Subsequent increase of the MSE mass at the same eigenfrequency decreases the thermal mechanical noise.

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Parallel-Data-Based Social Evolution Modeling

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Tsinghua Science and Technology 2021年第6期26卷 878-885页

作者： Weishan Zhang Zhaoxiang Hou Xiao Wang Zhidong Xu Xin Liu Fei-Yue Wang China University of Petroleum Qingdao 266580China Qingdao Academy of Intelligent Industry Qingdao 266111China State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100049China Institute of National Security National Defense UniversityBeijing 100081China

Abnormal or drastic changes in the natural environment may lead to unexpected events,such as tsunamis and earthquakes,which are becoming a major threat to national ***,no effective assessment approach can deduce a situation and determine the optimal response strategy when a natural disaster *** this study,we propose a social evolution modeling approach and construct a deduction model for self-playing,self-learning,and self-upgrading on the basis of the idea of parallel data and reinforcement *** proposed approach can evaluate the impact of an event,deduce the situation,and provide optimal strategies for *** the breakage of a submarine cable caused by earthquake as an example,we find that the proposed modeling approach can obtain a higher reward compared with other existing methods.

关键词： paral el data reinforcement learning decision-making

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Simulated Ising Annealing Algorithm with Gaussian Augmented Hamiltonian Monte Carlo

Simulated Ising Annealing Algorithm with Gaussian Augmented ...

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第42届中国控制会议

作者： Langyu Li Hao Wang Zhixin Xie Zixuan Liu Wei Cui Yu Pan Institute of Cyber-Systems and Control State Key Laboratory of Industrial Control TechnologyCollege of Control Science and Engineering Zhejiang University School of Automation Science and Engineering South China University of Technology School of Mechanical Engineering and Automation Harbin Institude of Technology

A numerical technique called Simulated Ising Annealing(SIA) uses digital computers to obtain approximations of the ground states of Ising models. The quadratic unconstrained binary optimization(QUBO) problem in combinatorial optimization,which is challenging to solve through exhaustive search, has significant linkages to the Ising model. Therefore, SIA's main objective is to identify a better solution in finite iterations. In order to enhance the performance of the simulated annealing procedure, we adopt a Gaussian augmented Hamiltonian Monte Carlo(GAHMC) method in this study. We contrasted our algorithm with Gibbs sampling and local quantum annealing method. Finally, we discuss its application on QUBO problem.

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Reference Tracking control of a Nonlinear Epidemiological Model with State Estimation

Reference Tracking Control of a Nonlinear Epidemiological Mo...

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International Conference on control, Decision and Information Technologies (CoDIT)

作者： Balázs Csutak Gábor Szederkényi Faculty of Information Technology and Bionics Pazmany Peter Catholic University Budapest Hungary Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

In this paper, a reference tracking controller for an 8-compartment epidemic model is proposed. The dynamical model describing the disease spread and progression is given in nonlinear input-affine form. The manipulable input is the transmission rate, while the output to be tracked is the number of infected people. The model parameters correspond to the COVID-19 pandemic. The control design uses a simple SEIR model and it is based on feedback linearization combined with extended Kalman filter for state estimation. Simulation results show good tracking performance even with model mismatch and significant parameter uncertainty.

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Exploring the use of deep learning in task-flexible ILC

arXiv

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arXiv 2023年

作者： Vinjarapu, Anantha Sai Hariharan Broens, Yorick Butler, Hans Tóth, Roland The Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands The Systems and Control Laboratory Institute for Computer Science and Control Hungary Asml Veldhoven Netherlands

Growing demands in today's industry results in increasingly stringent performance and throughput specifications. For accurate positioning of high-precision motion systems, feedforward control plays a crucial role. Nonetheless, conventional model-based feedforward approaches are no longer sufficient to satisfy the challenging performance requirements. An attractive method for systems with repetitive motion tasks is iterative learning control (ILC) due to its superior performance. However, for systems with non-repetitive motion tasks, ILC is generally not applicable, despite of some recent promising advances. In this paper, we aim to explore the use of deep learning to address the task flexibility constraint of ILC. For this purpose, a novel Task Analogy based Imitation Learning (TAIL)-ILC approach is developed. To benchmark the performance of the proposed approach, a simulation study is presented which compares the TAIL-ILC to classical model-based feedforward strategies and existing learning-based approaches, such as neural network based feedforward *** Codes 37M15 © 2023, CC BY.

关键词： Feedforward control

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AGV State Monitoring Based on Global and First-person View Fusion in Digital Workshops 12

AGV State Monitoring Based on Global and First-person View F...

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12th International Conference on CYBER Technology in automation, control, and Intelligent systems, CYBER 2022

作者： Zhang, Sichao Liang, Wei Li, Gengyu Yuan, Xudong An, Haibo Zhang, Yinlong State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Science Beijing100049 China School of Information and Control Engineering Shenyang Jianzhu University Shenyang110168 China Science and Technology on Information Systems Engineering Laboratory The 28th Research Institute of Cetc Jiangsu Nanjing210007 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

AGV state monitoring is becoming increasingly important for production system safety in digital workshops. This paper develops a global and first-person view fusion to monitor the AGV (Automated Guided Vehicle) running state in the complex digital workshops. Based on the affine transformation model, the AGV running states, i.e., position, velocity and orientation are estimated by the global camera with the assistance of ArUco markers, which are pasted on the front side of the AGV. Besides, the first-person view camera is used to implement pedestrian detection and distance measurement based on the YOLOv3 model. It is noticeable that the pitch angle is considered for distance measurement, and an improved triangular model for distance measurement is proposed. The effectiveness of the proposed method has been verified in our built platform. © 2022 IEEE.

关键词： Cameras

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