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arXiv 2022年

作者： Xu, Jun Chen, Fanglin School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen518055 China The Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China The Department of Computer Engineering Harbin Institute of Technology Shenzhen518055 China

control invariant set is critical for guaranteeing safe control and the problem of computing control invariant set for linear discrete-time system is revisited in this paper by using a data-driven approach. Specifically, sample points on convergent trajectories of linear MPC are recorded, of which the convex hull formulates a control invariant set for the linear system. To approximate the convex hull of multiple sample points, a convex piecewise linear (PWL) fitting framework has been proposed, which yields a polyhedral approximation with predefined complexity. A descent algorithm for the convex PWL fitting problem is also developed, which is guaranteed to converge to a local optimum. The proposed strategy is flexible in computing the control invariant set in high dimension with a predefined complexity. Simulation results show that the proposed data-driven approximation can compute the approximated control invariant set with high accuracy and relatively low computational cost. © 2022, CC0.

关键词： Linear systems

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Research on Geomembrane Leakage Detection Method Based on Quadruped Robot

Research on Geomembrane Leakage Detection Method Based on Qu...

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Chinese automation Congress (CAC)

作者： Pengcheng Zhang Ningning Hu Xiao Wang Qingbiao Xi Haohao Zhao Xingchen Wu Linfeng Qin Dongli Yuan Investigation & Testing Research Institute Shanghai Investigation Design & Research Institute Co. Ltd. Shanghai China System Department 2 Xi'an ASN Technology Group Co. Ltd. Xi'an China School of Electronic and Control Engineering Chang'an University Xi'an China School of Automation Northwestern Polytechnical University Xi'an China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Aiming at the problem of leakage detection of seepage control geomembranes in landfills, a fully autonomous dipole method detection system based on a quadruped robot equipped with a dipole sensor is designed. The feasibility and superiority of using a quadruped robot are analyzed on the basis of the detection principle of the dipole method. Due to the problems existing in the process of upgrading the artificial static detection to autonomous dynamic detection, a dynamic detection method based on a quadruped robot is designed while the structure construction of the dipole method on the quadruped robot is completed. The experimental results show that the efficiency of geomembrane leakage detection can be improved with detection system method employed in this paper.

关键词： Legged locomotion Costs automation Landfills Dogs Robot sensing systems Software Quadrupedal robots Intelligent control

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A Model-free False Data Injection Attack Strategy in Networked control systems

A Model-free False Data Injection Attack Strategy in Network...

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IEEE Conference on Decision and control

作者： Xiaoyu Luo Chongrong Fang Chengcheng Zhao Jianping He The Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China and Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China The State Key Laboratory of Industrial Control Technology and Institute of Cyberspace Research Zhejiang University China

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

The data-driven attack strategies recently have received much attention when the full knowledge of the system model is unknown or difficult to be obtained for the adversary. Note that despite the critical parameters of the system model being unavailable for the adversary, the existing data-driven attack methods still depend on the linearity of the unknown system model. In this paper, we design a completely model-free attack strategy where the adversary with limited capability aims to compromise state variables such that the output value follows the expected trajectory. Specifically, we first construct a zeroth-order feedback optimization framework and uninterruptedly use probing signals for real-time measurements. Then, we iteratively update the attack signals along the composite direction of the gradient estimates of the objective function evaluations and the projected gradients. These objective function evaluations can be obtained only by real-time measurements. Furthermore, we characterize the optimality of these solutions via the optimality gap, which is affected by the dimensions of the attack signal, the iterations of solutions, and the convergence rate of the system. Extensive simulations are conducted to show the effectiveness of the proposed attack strategy.

关键词： Networked control systems Computational modeling Current measurement Linear programming Real-time systems Data models Time measurement

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Privacy Preservation for Cloud-Edge-Collaborative Energy Management system Using Post-Quantum Homomorphic Encryption

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IEEE Transactions on Smart Grid 2025年

作者： Jiang, Cheng Li, Xue Du, Dajun Qian, Hong Wen, Mi Wu, Lei Findeisen, Rolf Shanghai University of Electric Power Shanghai Key Laboratory of Power Station Automation Technology College of Automation Engineering Shanghai200090 China Shanghai University Shanghai Key Laboratory of Power Station Automation Technology School of Mechatronical Engineering and Automation Shanghai200072 China Shanghai University of Electric Power College of Computer Science and Technology Shanghai201306 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610225 China Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ07030 United States TU Darmstadt Control and Cyber-Physical Systems Laboratory Darmstadt64227 Germany

Cloud-based energy management systems (EMS) in smart grids face privacy challenges, as existing methods based on traditional homomorphic encryption support limited operations and are vulnerable to quantum attacks. We propose a privacy preservation method for smart grid energy management system, leveraging edge-assisted computing and ring learning with errors (RLWE) homomorphic encryption. Our approach enables both addition and multiplication operations on encrypted smart grid data while defending against quantum threats. We demonstrate the methods security under the RLWE hardness assumption and validate its effectiveness through an experimental platform simulating smart grid environments. This work addresses computational limitations in current privacy-preserving techniques, enhancing data security for evolving smart grid technologies © 2010-2012 IEEE.

关键词： Power management (telecommunication)

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Semi-Global Leaderless Consensus of Linear Multi-agent systems with Actuator and Communication Constraints

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Journal of systems Science & Complexity 2020年第4期33卷 882-902页

作者： WANG Jian SHI Liangren GUAN Xinping Department of Automation and Key Laboratory of System Control and Information Processing of Ministry of EducationShanghai Jiao Tong UniversityShanghai 200240China Shanghai Key Laboratory of Navigation and Location-based Services and Academy of Information Technology and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China

This paper studies the leaderless consensus problems of multi-agent systems with input saturation and intermittent communication over directed networks. Both the state feedback and the output feedback consensus algorithms are developed based on low gain feedback approach. The convergence of the trajectories of all agents can be achieved by these proposed algorithms, if the communication topology has a directed spanning tree, and the intermittent communication period T and time rate ρ are larger than their associated threshold values. Simulation examples are provided to verify the theoretical results.

关键词： agent directed communication

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Pseudo-LiDAR for Visual Odometry

arXiv

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arXiv 2022年

作者： Deng, Huiying Wang, Guangming Feng, Zhiheng Jiang, Chaokang Wu, Xinrui Miao, Yanzi Wang, Hesheng Advanced Robotics Research Center AI Research Institute School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China

As one of the important tasks in the field of robotics and machine vision, LiDAR/visual odometry provides tremendous help for various applications such as navigation, location, etc. In the existing methods, LiDAR odometry shows superior performance, but visual odometry is still widely used for its price advantage. Conventionally, the task of visual odometry mainly rely on the input of continuous images. However, it is very complicated for the odometry network to learn the epipolar geometry information provided by the images. In this paper, the concept of pseudo-LiDAR is introduced into the odometry to solve this problem. The pseudo-LiDAR point cloud back-projects the depth map generated by the image into the 3D point cloud, which changes the way of image representation. Compared with the stereo images, the pseudo-LiDAR point cloud generated by the stereo matching network can get the explicit 3D coordinates. Since the 6 Degrees of Freedom (DoF) pose transformation occurs in 3D space, the 3D structure information provided by the pseudo-LiDAR point cloud is more direct than the image. Compared with sparse LiDAR, the pseudo-LiDAR has a denser point cloud. In order to make full use of the rich point cloud information provided by the pseudo-LiDAR, a projection-aware dense odometry pipeline is adopted. Most previous LiDAR-based algorithms sampled 8192 points from the point cloud as input to the odometry network. The projection-aware dense odometry pipeline takes all the pseudo-LiDAR point clouds generated from the images except for the error points as the input to the network. While making full use of the 3D geometric information in the images, the semantic information in the images is also used in the odometry task. The fusion of 2D-3D is achieved in an image-only based odometry. Experiments on the KITTI dataset prove the effectiveness of our method. To the best of our knowledge, this is the first visual odometry method using pseudo-LiDAR. Copyright © 2022, The Authors. A

关键词： Pipelines

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Joint Offloading Decision and Resource Allocation for Vehicular Fog-Edge Computing Networks: A Contract-Stackelberg Approach

arXiv

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arXiv 2022年

作者： Li, Yuwei Yang, Bo Wu, Hao Han, Qiaoni Chen, Cailian Guan, Xinping The Department of Automation Shanghai Jiao Tong University Shanghai200240 China Huawei Technologies Co. Ltd China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China The Department of Automation Tianjin University Tianjin 300072 China

With the popularity of mobile devices and development of computationally intensive applications, researchers are focusing on offloading computation to Mobile Edge Computing (MEC) server due to its high computational efficiency and low communication delay. As the computing resources of an MEC server are limited, vehicles in the urban area who have abundant idle resources should be fully utilized. However, offloading computing tasks to vehicles faces many challenging issues. In this paper, we introduce a vehicular fog-edge computing paradigm and formulate it as a multi-stage Stackelberg game to deal with these issues. Specifically, vehicles are not obligated to share resources, let alone disclose their private information (e.g., stay time and the amount of resources). Therefore, in the first stage, we design a contract-based incentive mechanism to motivate vehicles to contribute their idle resources. Next, due to the complicated interactions among vehicles, road-side unit (RSU), MEC server and mobile device users, it is challenging to coordinate the resources of all parties and design a transaction mechanism to make all entities benefit. In the second and third stages, based on Stackelberg game, we develop pricing strategies that maximize the utilities of all parties. The analytical forms of optimal strategies for each stage are given. Simulation results demonstrate the effectiveness of our proposed incentive mechanism, reveal the trends of energy consumption and offloading decisions of users with various parameters, and present the performance comparison between our framework and existing MEC offloading paradigm in vehicular networks. © 2022, CC BY.

关键词： Fog computing

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New Sensing Technologies for Monitoring Machinery,Structures,and Manufacturing Processes

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Journal of Dynamics, Monitoring and Diagnostics 2023年第2期2卷 69-88页

作者： JDMD Editorial Office Zhaoyan Fan Robert X.Gao Qingbo He Yi Huang Tianxi Jiang Zhike Peng Luc Thévenaz Yuyong Xiong Shuncong Zhong Editorial Office of JDMD Chongqing University of TechnologyChongqingPeople’s Republic of China Department of Mechanical Industrialand Manufacturing EngineeringOregon State UniversityCorvallisORUSA Department of Mechanical and Aerospace Engineering Case Western Reserve UniversityClevelandOHUSA State Key Laboratory of Mechanical System and Vibration Shanghai Jiao Tong UniversityShanghaiPeople’s Republic of China Fujian Provincial Key Laboratory of Terahertz Functional Devices and Intelligent Sensing School of Mechanical Engineering and AutomationFuzhou UniversityFuzhouPeople’s Republic of China Institute of Precision Instrument and Intelligent Measurement&Control Fuzhou UniversityFuzhouPeople’s Republic of China School of Mechanical Engineering Ningxia UniversityYinchuanPeople’s Republic of China EPFL Ecole Polytechnique Fédérale de Lausanne Group for Fibre OpticsSCI-STI-LT Station 111015 LausanneSwitzerland

Sensing is the fundamental technique for sensor data acquisition in monitoring the operation condition of the machinery,structures,and manufacturing *** this paper,we briefly discuss the general idea and advances of various new sensing technologies,including multiphysics sensing,smart materials and metamaterials sensing,microwave sensing,fiber optic sensors,and terahertz sensing,for measuring vibration,deformation,strain,acoustics,temperature,spectroscopic,*** on the observations from the state of the art,we provide comprehensive discussions on the possible opportunities and challenges of these new sensing technologies so as to steer future development.

关键词： fiber optic sensor metamaterials sensing microwave sensing multiphysics sensing terahertz sensing

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The DRTS-Net Framework: Enhanced Probabilistic Health Perception for Wind Turbine Bearings Under Complex Operating Conditions With Improved Interpretability

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Yang, Tongguang Li, Guanchen Huo, Jinzhao Li, Xiaoyong Zhou, Xianwen Han, Qingkai Northeastern University School of Mechanical Engineering and Automation Shenyang110819 China Johns Hopkins University Department of Mechanical Engineering BaltimoreMD21218 United States Foshan University School of Mechatronic Engineering and Automation Foshan528225 China SCNU Environmental Research Institute Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety MOE Key Laboratory of Theoretical Chemistry of Environment South China Normal University School of Environment Guangzhou510006 China SCNU Qingyuan Institute of Science and Technology Innovation Company Ltd. Qingyuan511517 China Northeastern University Key Laboratory of Vibration and Control of Aero-Propulsion System Ministry of Education Shenyang110819 China

Health perception in mechanical systems is a crucial aspect of prognostics and health management (PHM). Deep learning models have been successfully applied to health perception in wind turbine bearings. However, due to the time-varying, redundant, and noisy characteristics of time-series data representing bearing wear, existing models face significant challenges, particularly in terms of generalization and interpretability. Moreover, although most current models have provided health perceptions, they overlook the strong stochastic nature of bearing degradation data, and thus fail to deliver reliable predictions of uncertainty information. To address these technical challenges, this study introduces a novel Deep residual temporal-spatial framework (DRTS-Net) for probabilistic health perception of wind turbine bearing wear states. This article creatively designs spatiotemporal information fusion module for extracting features from bearing degradation data. Inspired by improvements in residual network structures, the DRTS-Net integrates an attention mechanism and a soft thresholding mechanism from the deep residual shrinkage network to optimize the feature extraction process. This integration aims to mitigate the interference from minor redundant features in the degradation data. The framework incorporates Gaussian process regression in the output layer to yield the probability distribution of single-health perception values for bearing features. A comparative study was conducted using the same bearing health state dataset against current mainstream deep learning methods for bearing health assessment. Results demonstrate that the proposed DRTS-Net achieves superior performance in high-precision health perception, high-reliability probabilistic health perception. © 1963-2012 IEEE.

关键词： Stochastic systems

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Learning Pre-Grasp Manipulation of Flat Objects in Cluttered Environments using Sliding Primitives

Learning Pre-Grasp Manipulation of Flat Objects in Cluttered...

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IEEE International Conference on Robotics and automation (ICRA)

作者： Jiaxi Wu Haoran Wu Shanlin Zhong Quqin Sun Yinlin Li State Key Laboratory of Turbulence and Complex Systems Intelligent Biomimetic Design Lab College of Engineering Peking University Beijing China Department of Automation University of Science and Technology of China Heifei China State Key Laboratory of Management and Control for Complex System Institute of Automation Chinese Academy of Science Beijing China Science and Technology on Thermal Energy and Power Laboratory Wuhan China

Flat objects with negligible thicknesses like books and disks are challenging to be grasped by the robot because of the width limit of the robot's gripper, especially when they are in cluttered environments. Pre-grasp manipulation is conducive to rearranging objects on the table and moving the flat objects to the table edge, making them graspable. In this paper, we formulate this task as Parameterized Action Markov Decision Process, and a novel method based on deep reinforcement learning is proposed to address this problem by introducing sliding primitives as actions. A weight-sharing policy network is utilized to predict the sliding primitive's parameters for each object, and a Q-network is adopted to select the acted object among all the candidates on the table. Meanwhile, via integrating a curriculum learning scheme, our method can be scaled to cluttered environments with more objects. In both simulation and real-world experiments, our method surpasses the existing methods and achieves pre-grasp manipulation with higher task success rates and fewer action steps. Without fine-tuning, it can be generalized to novel shapes and household objects with more than 85% success rates in the real world. Videos and supplementary materials are available at https://***/view/pre-grasp-sliding.

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