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International Conference on Guidance, Navigation and control, ICGNC 2022

作者： Dai, Shijie Wu, Zhengxing Tan, Min Yu, Junzhi School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China State Key Laboratory for Turbulence and Complex System Department of Advanced Manufacturing and Robotics BIC-ESAT College of Engineering Peking University Beijing100871 China

ISBN: (纸本)9789811966125

This paper investigates the formation control problem for a multi-robotic fish system and a distributed event-triggered-based formation control framework is proposed. The framework consists of a communication topology, a distributed formation control law, and a dynamic event-triggered communication mechanism. In particular, a position-based formation control law is presented to drive the multi-robotic fish system to an anticipated configuration based on local measurements while keeping pace with a moving target. Meanwhile, a dynamic event-triggered mechanism is utilized to measure importance of the interactive information and decide the communication timing. The proposed formation control method shows its merits to greatly reduce communication consumption with a limited loss of formation control performance. Finally, simulations with quantitative comparisons are presented to verify the effectiveness of the proposed framework. This formation control framework provides a solid foundation for future marine cooperative control of the multi-robotic fish system. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Multi agent systems

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Input Constrained Prescribed Performance-Based control for Hypersonic Gliding Vehicle

SSRN

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SSRN 2025年

作者： Zheng, Kefan Zhao, Xinyue Zeng, Xianlin Zhang, Xiaozhen Fang, Hao Hong, Yiguang National Key Laboratory of Autonomous Intelligent Unmanned Systems Beijing Institute of Technology Beijing100081 China School of Automation Beijing Institute of Technology Beijing100081 China Research Institute for Intelligent Autonomous Systems Tongji University Shanghai200092 China Department of Control Science and Engineering Tongji University Shanghai200092 China

This paper proposes an input constrained prescribed performance-based control (IC-PPC) scheme to investigate the tracking control problem of hypersonic glide vehicle (HGV) system simultaneously considering the user-specified tracking performance and control input constraints. To overcome the challenges caused by the high nonlinearity of the aerodynamic coefficients, the dynamic of HGV system is transformed into the strict-feedback form through the introduction of virtual inputs. A novel decoupling method is developed for the separation of the non-convex coupling constraints of virtual inputs and the calculation of the input saturation errors (the errors between actual and desired inputs). To avoid the violation of the user-specified tracking performance due to the input saturation, an auxiliary system is developed to generate the modified signals. These signals are guaranteed to be non-negative and used to create the input constrained performance functions (IC-PFs), which are capable to degrade or recover the performance boundaries depending on different saturation conditions. Consequently, it is theoretically shown that the output can always track the desired trajectories and satisfy the constraints of the IC-PFs. Finally, comparative simulations are presented to showcase these theoretical findings. © 2025, The Authors. All rights reserved.

关键词： Three term control systems

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Data-driven soft sensors in blast furnace ironmaking:a survey

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Frontiers of Information technology & Electronic engineering 2023年第3期24卷 327-354页

作者： Yueyang LUO Xinmin ZHANG Manabu KANO Long DENG Chunjie YANG Zhihuan SONG State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhou 310027China Department of Systems Science Kyoto UniversityKyoto 606-8501Japan Research Institute Baoshan Iron&Steel Co.Ltd.Shanghai 201900China

The blast furnace is a highly energy-intensive,highly polluting,and extremely complex reactor in the ironmaking *** sensors are a key technology for predicting molten iron quality indices reﬂecting blast furnace energy consumption and operation stability,and play an important role in saving energy,reducing emissions,improving product quality,and producing economic *** the advancement of the Internet of Things,big data,and artificial intelligence,data-driven soft sensors in blast furnace ironmaking processes have attracted increasing attention from researchers,but there has been no systematic review of the data-driven soft sensors in the blast furnace ironmaking *** review covers the state-of-the-art studies of data-driven soft sensors technologies in the blast furnace ironmaking ***,wefirst conduct a comprehensive overview of various data-driven soft sensor modeling methods(multiscale methods,adaptive methods,deep learning,etc.)used in blast furnace ***,the important applications of data-driven soft sensors in blast furnace ironmaking(silicon content,molten iron temperature,gas utilization rate,etc.)are ***,the potential challenges and future development trends of data-driven soft sensors in blast furnace ironmaking applications are discussed,including digital twin,multi-source data fusion,and carbon peaking and carbon neutrality.

关键词： Soft sensors Data-driven modeling Machine learning Deep learning Blast furnace Ironmaking

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Intelligent Monitoring Based on Integrated Temporal Dictionary Learning with Isometric Mapping for Geological Drilling Process

Intelligent Monitoring Based on Integrated Temporal Dictiona...

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IEEE International Conference on Industrial technology (ICIT)

作者： Tianyu Ma Sheng Du Haipeng Fan Cheng Huang School of Future Technology China University of Geosciences Wuhan China School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

Geological drilling process, owing to Complex geological environment and harsh downhole conditions, generates data including characteristics such as pressure, rotational speed, and depth, which are frequently high-dimensional and noisy. These characteristics make real-time monitoring more complex. Existing methods in the geological drilling process, such as rule-based systems and threshold techniques, struggle to handle the complexity and high dimensionality of drilling data, leading to high false alarm rates and low detection accuracy. This paper develops an integrated temporal dictionary learning with isometric mapping method for monitoring geological drilling process. Specifically, Isometric Mapping is employed to perform dimensionality reduction on high-dimensional data, thereby retaining the structural features in the lower-dimensional space. Subsequently, Lasso regularization is applied for sparse coding to extract essential features from the reduced data. To address the fluctuations arising from the iterative dictionary learning process, a temporal smoothing term is incorporated to ensure the stability of the dictionary across different time steps. After that, the reconstruction errors were adopted to achieve comprehensive statistical indicators. Then the overall monitoring was realized for the plant-wide process. The effectiveness and robustness of the proposed method are demonstrated through case studies on the Tennessee-Eastman process and the actual geothermal drilling process.

关键词： Drilling Smoothing methods Accuracy Geology Velocity control Feature extraction Robustness Kernel Monitoring Principal component analysis

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A Low-Cost Enhanced Heterogeneous Barrier Construction Method for Multi-Target Intrusion Monitoring

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IEEE Sensors Journal 2025年

作者： Ye, Jing Xiong, Yonghua Dong, Haobin Yu, Anjun She, Jinhua China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China Jiangxi Ganyue Expressway Company Ltd Nanchang330200 China Tokyo University of Technology School of Engineering Tokyo192-0982 Japan

In multi-target intrusion monitoring, heterogeneous nodes may overlap due to different sensing ranges. Additionally, excessively long barriers will increase the cost of barriers and reduce the number of barriers. To address these issues, this article proposes a distributed method for constructing weighted heterogeneous barriers using depth-first search and learning automata (WHBDLA). It designs a dynamically weighted coverage graph based on the non-overlap rate and horizontal displacement. The proposed method considers multiple heterogeneous properties. It uses several intrusion targets to identify the boundaries of local sub-regions while avoiding the cost increase and quantity decrease caused by long barriers. WHBDLA combines the advantages of depth-first search and the reinforcement learning mechanism to adaptively construct heterogeneous barriers in sub-regions with unknown faults. The algorithm initially searches for potential barrier path nodes and then activates nodes with sufficient energy and high weights. Finally, it monitors and repairs the barriers. Comparative experiments and analyses indicate that the proposed method increases the number of barriers, reduces node costs, and decreases running time. © 2001-2012 IEEE.

关键词： Reinforcement learning

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Motion Planning of 3D Nonholonomic Robots via Curvature-Constrained Vector Fields

Motion Planning of 3D Nonholonomic Robots via Curvature-Cons...

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IEEE Conference on Decision and control

作者： Yike Qiao Xiaodong He Zhongkui Li Department of Mechanics and Engineering Science College of Engineering State Key Laboratory for Turbulence and Complex Systems Peking University Beijing China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Vector-field-based methods are typical feedback planning algorithms, especially eligible for the motion planning of nonholonomic robots. Nevertheless, most existing vector fields (VF) do not account for the prevalent constraints on robot’s kinematics. This paper addresses the motion planning problem for 3D nonholonomic robots with trajectories featuring upper bounded curvature. To this end, a curvature-constrained VF over $\mathbb{R}^{3}$ is proposed, whose integral curves guarantee an upperbound of curvature as well as an almost-global attraction region towards the desired position with a specified heading direction. Moreover, a control strategy is presented to determine the robot’s control inputs subject to the curvature constraint. Under the designed control laws, the robot is guaranteed to track the VF while ensuring that the actual trajectory adheres to the curvature constraint. Finally, the efficacy of the presented motion planning algorithm is validated by numerical simulations.

关键词： Three-dimensional displays Tracking Velocity control Kinematics Numerical simulation Vectors Planning Trajectory Collision avoidance Robots

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Autonomous Microgrid Restoration Using Grid-Forming Inverters and Smart Circuit Breakers

Autonomous Microgrid Restoration Using Grid-Forming Inverter...

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2022 IEEE Power and Energy Society General Meeting, PESGM 2022

作者： Banerjee, Abhishek Pandey, Amit Pailla, Uttam Reddy Seo, Gab-Su Shekhar, Shashank Jain, Himanshu Lin, Yashen Wu, Xiaofan Bamberger, Joachim Muenz, Ulrich Autonomous Systems and Control Siemens Technology PrincetonNJ08540 United States Power Systems Engineering Center National Renewable Energy Laboratory GoldenCO80401 United States Indian Institute of Technology Department of Hydro and Renewable Energy Roorkee India

ISBN: (数字)9781665408233

ISBN: (纸本)9781665408233

The proliferation of distributed inverter-based resources (IBRs) raises the questions if these IBRs can be used to blackstart microgrids and distribution feeders after major outages. In this paper, we propose and evaluate an autonomous microgrid restoration concept using grid-forming (GFM) IBRs and smart circuit breakers (SCBs). The concept is first explored in simulation platform and then a hardware testbed containing actual GFM inverters is developed to demonstrate these functionalities. A combination of dispatchable virtual oscillator control (dVOC) and droop-based control schemes have been designed for GFM inverter controls in the software simulations and hardware testbed. Subsequently, operation of SCBs using two distinct principles have been demonstrated that can restore or connect portions of the network. © 2022 IEEE.

关键词： Restoration

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DSTFormer: 3D Human Pose Estimation with a Dual-scale Spatial and Temporal Transformer Network

DSTFormer: 3D Human Pose Estimation with a Dual-scale Spatia...

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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Shaokun Zhang Xinde Li Chuanfei Hu Jianping Xu Huaping Liu School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China Nanjing Center for Applied Mathematics Nanjing China Southeast University Shenzhen Research Institute Shenzhen China Science and Technology on Information Systems Engineering Laboratory Nanjing China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

Recent transformer-based methods for estimating 3D human pose have gained widespread attention, achieving state-of-the-art results. Previous methods have primarily focused on capturing motion patterns of the human body at a single scale or cascading multiple scales, such as joints, bones, and body-parts. However, they are difficult to simultaneously capture spatial-temporal motion patterns of the human body at different scales due to the complex motion patterns. To address this issue, we propose Dual-scale Spatial and Temporal transFormer (DSTFormer), which can concurrently explore the spatial dependencies and temporal motion patterns of human joints and bones. Additionally, we introduce a Gcn-Spatial Transformer Block (GSTB), which introduces Graph Convolutional Networks (GCN) into transformer to enhance the exploitation of local relationships and global information between adjacent joints or bones. Extensive experiments are conducted on the Human3.6M benchmark dataset, and superior results are reported when comparing to other state-of-the-art methods. More remarkably, our model achieves to-date the best published performance, with P1 errors of 37.9 mm and 15.6 mm, respectively.

关键词： Solid modeling Three-dimensional displays Mechatronics Graph convolutional networks Computational modeling Pose estimation Transformers Bones Joints Robots

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PDAVIS: Bio-inspired Polarization Event Camera

PDAVIS: Bio-inspired Polarization Event Camera

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2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Haessig, Germain Joubert, Damien Haque, Justin Milde, Moritz B. Delbruck, Tobi Gruev, Viktor Center for Vision Automation & Control High-Performance Vision Systems AIT Austrian Institute of Technology Vienna Austria University of Zurich Institute of Neuroinformatics ETH Zurich Switzerland Western Sydney University International Centre for Neuromorphic Systems The MARCS Institute Sydney Australia University of Illinois at Urbana-Champaign Department of Electrical and Computer Engineering UrbanaIL United States

ISBN: (纸本)9798350302493

The stomatopod (mantis shrimp) visual system has recently provided a blueprint for the design of paradigm-shifting polarization and multispectral imaging sensors, enabling solutions to challenging medical and remote sensing problems. However, these bioinspired frame-based cameras lack the high dynamic range and asynchronous polarization vision capabilities of the stomatopod visual system, limiting temporal resolution to ~12ms and dynamic range to ~72dB. Here we present a novel stomatopod-inspired polarization camera which mimics the sustained and transient biological visual pathways to save power and sample data beyond the maximum Nyquist frame rate. This bio-inspired sensor simultaneously captures both synchronous intensity frames and asynchronous polarization brightness change information with submillisecond latencies over a millionfold range of illumination. Our PDAVIS camera is comprised of 346x260 pixels, organized in 2-by-2 macropixels, which filter the incoming light with four linear polarization filters offset by 45°. Polarization information is reconstructed using both low-cost and low-latency event-based algorithms and more accurate but slower deep neural networks. Our sensor is used to image high dynamic range polarization scenes that vary at high speeds and to observe the dynamical properties of single collagen fibers in a bovine tendon under rapid cyclical ***: https://***/mFuCeTMWEqY © 2023 IEEE.

关键词： Polarization

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Data-driven Model Based Online Fault Detection Using OMP-ERR 4

Data-driven Model Based Online Fault Detection Using OMP-ERR

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4th International Conference on Industrial Artificial Intelligence, IAI 2022

作者： Zhou, Guangze Luo, Zhong Zhu, Yunpeng Gao, Yi Wang, Zhiao Northeastern University School of Mechanical Engineering and Automation Shenyang110819 China Northeastern University Key Laboratory of Vibration and Control of Aero-Propulsion Systems Ministry of Education of China Shenyang110819 China Dalian University of Technology State Key Laboratory of Structural Analysis for Industrial Equipment Dalian116024 China University of Sheffield Department of Automatic Control and System Engineering SheffieldS13JD United Kingdom

ISBN: (数字)9781665451208

ISBN: (纸本)9781665451208

Model based online fault detection often conducted by extracting features from models driven by system input and output data under various working conditions. The efficiency of online system modelling is therefore significant to improve the performance of online fault detections. In this study, a novel fast data-driven modelling approach, known as the OMP (Orthogonal Matching Pursuit)- ERR (Error Reduction Ratio) method is proposed to improve the efficiency of online fault detections. The new system identification method is motivated by noticing that the traditional OMP algorithm is much faster but usually less accurate than the OLS (Orthogonal least squares) algorithm in the identification of system NARX (Nonlinear Auto-Regressive with Exogenous inputs) models. The problem is first illustrated by the identification of a Single Degree of Freedom (SDoF) system. After that, the OMP-ERR algorithm is developed to improve the NARX modelling efficiency for the purpose of system model-based online fault detections. A case study on the crack detection of a cantilever beam shows that the new approach is over 10 times faster than the traditional OLS modelling process, demonstrating the promising applications of the new approach in online fault detections in engineering practice. © 2022 IEEE.

关键词： Least squares approximations

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