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Asynchronous fault detection for delayed semi-markov jump systems with mismatched mode-dependent nonlinearities

INFORMATION SCIENCES 2022年 587卷 679-691页

作者： Ma, Chao Fu, Hang Wu, Wei Univ Sci & Technol Beijing Sch Mech Engn Beijing 100083 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

This paper deals with fault detection problem of delayed semi-Markov jump systems with disturbances. More specifically, the asynchronous phenomenon is taken into account between modes of linear and nonlinear dynamics. Accordingly, an efficient solution of fault detection is developed based on asynchronous mode-dependent fault filter with condi-tional probability. On the basis of constructing appropriate Lyapunov-Krasovskii functions, sufficient criteria are established such that H-infinity performance can be satisfied for filter error with residual signal. Furthermore, the devised filter gains are computed in terms of convex optimization. Eventually, the applicability of our proposed design is verified by an illustra-tive example. (C) 2021 Elsevier Inc. All rights reserved.

关键词： Asynchronous fault detection Semi-Markov jump systems Mismatched mode-dependent nonlinearities Time-varying delay

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The ChatGPT After: Building Knowledge Factories for Knowledge Workers with Knowledge Automation

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IEEE/CAA Journal of Automatica Sinica 2023年第11期10卷 2041-2044页

作者： Yutong Wang Xiao Wang Xingxia Wang Jing Yang Oliver Kwan Lingxi Li Fei-Yue Wang State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China School of Artificial Intelligence Anhui UniversityHefei 230039China Motion G Inc068898Singapore Department of Electrical and Computer Engineering Purdue School of Engineering and TechnologyIndiana University-Purdue University IndianapolisIndiana 46202-5132 USA IEEE

Briefing: This perspective introduces the concept and framework of knowledge factories with knowledge machines for knowledge workers to achieve knowledge automation for Industry 5.0 and intelligent *** The big hit of Chat GPT makes it imperative to contemplate the practical applications of big or foundation models [1]-[5]. However, as compared to conventional models, there is now an increasingly urgent need for foundation intelligence of foundation models for real-world industrial applications.

关键词： Automation template Chat GPT

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Stealthy FDI Attacks Against Networked control systems Using Two Filters With an Arbitrary Gain

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IEEE TRANSACTIONS ON CIRCUITS AND systems II-EXPRESS BRIEFS 2022年第7期69卷 3219-3223页

作者： Guo, Haibin Sun, Jian Pang, Zhong-Hua Beijing Inst Technol Sch Automat State Key Lab Intelligent Control & Decis Complex Beijing 100081 Peoples R China Beijing Inst Technol Chongqing Innovat Ctr Chongqing 401120 Peoples R China North China Univ Technol Key Lab Fieldbus Technol & Automat Beijing Beijing 100144 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

This brief investigates a stealthy false data injection (FDI) attack scheme against two communication channels to damage the performance of a networked control system. Compared with most existing works, a more practical and reasonable assumption that an attacker only needs to know the physical system knowledge is considered. By using two filters with an arbitrary gain that can be different from the filter gain of the system, a novel feedback channel attack scheme is proposed to ensure the attack process is stealthy, and the forward channel attack signal can be flexibly designed according to specific attack purposes. It is proved that the proposed attack scheme can render the closed-loop system unstable while bypassing a residual-based detector. Two cases of simulation results are provided to verify the theoretical analysis results.

关键词： Detectors Information filters Networked control systems Communication channels Circuits and systems Automation Sun Kalman filter networked control systems residual-based detector stealthy FDI attacks

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Development and Stiffness Optimization for a Flexible-Tail Robotic Fish

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IEEE ROBOTICS AND AUTOMATION LETTERS 2022年第2期7卷 834-841页

作者： Lu, Ben Zhou, Chao Wang, Jian Fu, Yuzhuo Cheng, Long Tan, Min Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

The integral flexible tail has the potential advantage of lifelike undulating motion. However, due to the complex manufacturing process and difficult modification of structural parameters, its application in robotic fish encounters many challenges. Combining rigid structure and flexible material, this letter proposes a passive flexible fish tail, which can achieve continuous movement and high swimming frequency with simple but effective structure. First, with the full consideration of bending deformation of the spring steels, a dynamic model is established. Next, a passive fitting method is particularly applied to imitate the traveling wave model of the carangiform fish. More importantly, a calculation model, which can be used to acquire the theoretical ranges of the stiffness of spring steels, is derived based on the bending model of the cantilever beam subjected to the concentrated force and moment. Finally, the extensive simulation and experiments validate the effectiveness of the proposed methods, and the designed robotic fish can achieve 0.77 m/s (i.e., 1.12 BL/s) at a swimming frequency of 2.5 Hz. The obtained results can provide a valuable sight for improving the swimming performance of the robotic fish.

关键词： Biomimetics biologically-inspired robots mechanism design

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Image Segmentation of Cabin Assembly Scene Based on Improved RGB-D Mask R-CNN

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2022年 71卷 1-12页

作者： Fu, Yichen Fan, Junfeng Xing, Shiyu Wang, Zhe Jing, Fengshui Tan, Min Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

Cabin pose measurement is one of the key procedures in the assembly and docking process of large cabins, which provides important feedback information for the subsequent docking control system. As the basis of cabin pose measurement, the accuracy and robustness of cabin assembly image segmentation are particularly important. However, traditional image segmentation method based on RGB sensor is extremely susceptible to interference from the external environment, which greatly weakens the recognition effect. In this article, an image segmentation method of cabin assembly scene based on improved red-green-blue-depth (RGB-D) Mask R-CNN is proposed, and its network structure is designed to be able to specifically process four-channel images. The method can accurately extract the corresponding area of the cabin under complex and severe environmental disturbances, with high robustness and generalization capability. Meanwhile, the excellence of deep learning segmentation algorithms with depth channel information input is highlighted. In experiments, improved classic segmentation network U-Net, SegNet, pyramid scene parsing network (PSPNet), and Deeplab-v3 based on RGB-D were constructed as control, and these models were tested and evaluated on the enhanced test sets to verify their segmentation accuracy and robustness performance. Comparing experiments fully demonstrate the superiority of the segmentation network model of RGB-D four-channel input over RGB input. At the same time, vision system using the proposed Mask R-CNN algorithm based on RGB-D has the best cabin segmentation accuracy, robustness, and generalization capability, which has practical significance for industrial applications.

关键词： Image segmentation Robustness Production Position measurement Feature extraction Deep learning Adaptation models Cabin docking cabin pose measurement deep neural network (DNN) red-green-blue-depth (RGB-D) image segmentation RGB-D sensor

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Dynamic Modeling and Performance Analysis for a Wire-Driven Elastic Robotic Fish

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IEEE ROBOTICS AND AUTOMATION LETTERS 2022年第4期7卷 11174-11181页

作者： Liao, Xiaocun Zhou, Chao Zou, Qianqian Wang, Jian Lu, Ben Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

The complex and continuous undulation of fishtail facilitates extraordinary underwater motion performance for natural fish. For the widely used Multi-Joint robotic fish, a lot of joints are used to simulate continuum fishtail, resulting in some challenges, e.g., the mechanism complexity, friction losses of adjacent joints, load disequilibrium and unsmooth servomotor output power. To overcome these intractable hurdles, motivated by natural fish, this letter proposes a wire-driven elastic robotic fish, which simulates fish muscle through multi-wire drive and adopts a fishlike spine design based on elastic component. Compared with the existing wire-driven robotic fish with discrete multiple-joints-spine and single-wire drive, our robotic fish not only has continuum fishtail, but also can swing with C-Shape and S-Shape owing to multi-wire coupling drive, and simulate the energy storage behavior of fish by elastic component. Further, a Lagrangian dynamic model that models a robotic fish with continuum fishtail and passive flexible joint is developed to explore the propulsive performance and validated by extensive experiments and simulations, and our robotic fish reaches the maximum swimming speed of 0.58 m/s, i.e., 1.04 BL/s. Finally, the superiority of the proposed drive mechanism in disposing of load disequilibrium and smoothing output power of servomotor, is analyzed and validated by the comparisons between a Multi-Joint robotic fish and our robotic fish.

关键词： Elastic component Lagrangian dynamics robotic fish wire-driven mode

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Design and Performance Analysis of a Series-Parallel Self-Aligning Index Finger Exoskeleton

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IEEE Robotics and Automation Letters 2025年第6期10卷 6456-6463页

作者： Sun, Ning Wang, Zelong Cheng, Long Li, Houcheng Niu, Ben Sun, Xijin Li, Ke Shandong Normal University School of Information Science and Engineering Jinan China Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Institute of Automation Beijing China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing China Shandong University Institute of Intelligent Medical Engineering School of Control Science and Engineering Jinan China

Hand exoskeletons have become increasingly crucial for the rehabilitation of hand function, as relevant studies have shown that using the exoskeletons to assist in rehabilitation training can improve hand motor function. However, developing a human-robot kinematic compatibility exoskeleton while providing sufficient assistance torque is still a challenge. This paper develops a series-parallel self-aligning index finger exoskeleton to assist the index finger in flexion/extension and adduction/abduction training. A series-parallel mechanism driven by two motors simultaneously was proposed for the metacarpophalangeal (MCP) joint, which can ensure human-robot joint axes self-alignment and increase the joint assistance torque. A kinematic compatibility analysis method was proposed, and the kinematic compatibility of the series-parallel mechanism was analyzed. The degrees of freedom and forward kinematics of the proposed mechanism were analyzed, and its workspace was calculated, which results show that the motion range of the MCP joint can meet the grasping training requirements. Preliminary experiments were carried out, and the results indicate that the motion trajectory coefficients of the marker points at the MCP, PIP, and DIP joints averaged for all subjects with and without the proposed exoskeleton are 0.993, 0.956, and 0.939, respectively, which can verify the kinematic transparency of the proposed mechanism. The assistance torque generated by the proposed mechanism on the MCP joint can meet the joint assistance torque requirements. In a word, the proposed exoskeleton can achieve flexion/extension and adduction/abduction training, human-robot joint axes self-alignment, and provide sufficient assistance torque. © 2016 IEEE.

关键词： Exoskeleton (Robotics)

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HMDRL: Hierarchical Mixed Deep Reinforcement Learning to Balance Vehicle Supply and Demand

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION systems 2022年第11期23卷 21861-21872页

作者： Xi, Jinhao Zhu, Fenghua Ye, Peijun Lv, Yisheng Tang, Haina Wang, Fei-Yue Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

The imbalance of vehicle supply and demand is a common phenomenon that influences the efficiency of online ride-hailing systems greatly. To address this problem, a three-level hierarchical mixed deep reinforcement learning method (HMDRL) is proposed to reposition idle vehicles. A manager operates at the top level, where action-abstraction is conducted from the time dimension and is adaptive for spatially scalable and time-varying systems. Coordinators locate at the middle level and a parallel coordination mechanism that is independent of the decision order is designed to improve the efficiency of the repositioning. The bottom level is composed of executive workers to reposition vehicles with mixed states and the states contain spatiotemporal information of agents' neighbor areas. Two reward functions are designed for the manager and the coordinators, respectively, aiming to improve the training effect by avoiding sparse rewards. A simulator based on real orders is designed and HMDRL is compared with six methods. Experimental results demonstrate that HMDRL outperforms all the other methods. In three comparison experiments, the order response rate is increased by 0.62% to 8.29%, 1.5% to 7.78%, 1.18% to 4.75%, respectively.

关键词： Reinforcement learning Pricing Heuristic algorithms Supply and demand Surges Vehicle dynamics Vehicles Deep reinforcement learning online ride-hailing system hierarchical repositioning framework parallel coordination mechanism mixed state

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Benefit from public unlabeled data: A Frangi filter-based pretraining network for 3D cerebrovascular segmentation

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MEDICAL IMAGE ANALYSIS 2025年 101卷 103442页

作者： Shi, Gen Lu, Hao Hui, Hui Tian, Jie Beihang Univ Sch Engn Med Beijing 100191 Peoples R China Beihang Univ Sch Biol Sci & Med Engn Beijing 100191 Peoples R China Beihang Univ Key Lab Big DataBased Precis Med Minist Ind & Informat Technol China Beijing 100191 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 10086 Peoples R China Chinese Acad Sci Inst Automat CAS Key Lab Mol Imaging Beijing 100190 Peoples R China Natl Key Lab Kidney Dis Beijing 100853 Peoples R China

Precise cerebrovascular segmentation in Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) data is crucial for computer-aided clinical diagnosis. The sparse distribution of cerebrovascular structures within TOF-MRA images often results in high costs for manual data labeling. Leveraging unlabeled TOF-MRA data can significantly enhance model performance. In this study, we have constructed the largest preprocessed unlabeled TOF-MRA dataset to date, comprising 1510 subjects. Additionally, we provide manually annotated segmentation masks for 113 subjects based on existing external image datasets to facilitate evaluation. We propose a simple yet effective pretraining strategy utilizing the Frangi filter, known for its capability to enhance vessel-like structures, to optimize the use of the unlabeled data for 3D cerebrovascular segmentation. This involves a Frangi filter-based preprocessing workflow tailored for large-scale unlabeled datasets and a multi-task pretraining strategy to efficiently utilize the preprocessed data. This approach ensures maximal extraction of useful knowledge from the unlabeled data. The efficacy of the pretrained model is assessed across four cerebrovascular segmentation datasets, where it demonstrates superior performance, improving the clDice metric by approximately 2%-3% compared to the latest semi- and self-supervised methods. Additionally, ablation studies validate the generalizability and effectiveness of our pretraining method across various backbone structures. The code and data have been open source at: https://***/shigen-StoneRoot/FFPN.

关键词： Cerebrovascular segmentation Self-supervised learning Frangi filter

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BiFNet: Bidirectional Fusion Network for Road Segmentation

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第9期52卷 8617-8628页

作者： Li, Haoran Chen, Yaran Zhang, Qichao Zhao, Dongbin Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Coll Artificial Intelligence Beijing 100049 Peoples R China

Multisensor fusion-based road segmentation plays an important role in the intelligent driving system since it provides a drivable area. The existing mainstream fusion method is mainly to feature fusion in the image space domain which causes the perspective compression of the road and damages the performance of the distant road. Considering the bird's eye views (BEVs) of the LiDAR remains the space structure in the horizontal plane, this article proposes a bidirectional fusion network (BiFNet) to fuse the image and BEV of the point cloud. The network consists of two modules: 1) the dense space transformation (DST) module, which solves the mutual conversion between the camera image space and BEV space and 2) the context-based feature fusion module, which fuses the different sensors information based on the scenes from corresponding features. This method has achieved competitive results on the KITTI dataset.

关键词： Roads Image segmentation Three-dimensional displays Cameras Laser radar Fuses Feature extraction Adaptive learning autonomous vehicles multisensor fusion road segmentation

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