检索结果-内蒙古大学图书馆

作者： Wu, Xiao-long Xu, Yuan-wu Xue, Tao Shuai, Junchao Jiang, Jianhua Deng, Zhonghua Fu, Xiaowei Li, Xi School of Automation Key Laboratory of Education Ministry for Image Processing and Intelligent Control Huazhong University of Science & Technology Wuhan China College of Computer Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan China Wuhan Industrial Economic Development Research Center Wuhan China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen China

As the solid oxide fuel cell (SOFC) system work environment is a high-temperature environment for a long time, it is difficult to obtain the SOFC stack internal state change directly. When the fault occurs, it is difficult to determine where the fault occurs. Moreover, the existing literature ignores the impact of faults, which creates many problems for SOFC system control. Therefore, a state observer-based fault detection method, which is used to detect the input flow sensor fault and the fuel input fault, is proposed. Their advantage is that they do not need data processing. To realize the fault detection, the observer is used to track the changes of SOFC stack chamber temperature. To obtain the observer estimation parameter, an approach from the actual stack structure parameters is employed to approximate the observer parameters. The results show the proposed fault detect method can judge fuel input fault type quickly and shield the disturbances signals from the sensor effectively. The proposed method also can be used to other operating points or air input fault. © 2018 Chinese Automatic control Society and John Wiley & Sons Australia, Ltd

关键词： Fault detection

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UAV Trajectory Generation Based on Integration of RRT and Minimum Snap Algorithms

UAV Trajectory Generation Based on Integration of RRT and Mi...

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Chinese automation Congress (CAC)

作者： Bohui Shi Youmin Zhang Lingxia Mu Jing Huang Jing Xin Yingmin Yi Shangbin Jiao Guo Xie Han Liu Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an Shaanxi China Industrial and Aerospace Engineering Concordia University Montreal Quebec Canada

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

Aiming at the problems that carrying out forest fire monitoring and fighting missions by using the Rapidly-exploring Randomized Tree (RRT) algorithm to plan paths cannot adapt to the autonomous movement of an Unmanned Aerial Vehicle (UAV) and that high-order dynamic characteristics may mutate during the mission, this paper investigates a trajectory generation algorithm based on the integration of the RRT algorithm and the minimum snap algorithm. First, the RRT algorithm is used to generate the initial path, then the minimum snap algorithm is used to smooth the initial path and obtain a trajectory suitable for the actual flight of the UAV. However, because the UAV is considered as a particle in the simulation, during the actual flight, this trajectory may not guarantee the safe flight of the UAV and may cause the UAV to collide with an obstacle or other nearby UAVs in the cases of formation flight. To solve this problem, flight corridor concept is used to limit the UAV's flight trajectory for ensuring the safe flight of the UAV. Simulation results show that the algorithm can effectively ensure the safety, smoothness, feasibility, and trajectory of unmanned aerial vehicles.

关键词： Vegetation Forestry Path planning Unmanned aerial vehicles Heuristic algorithms Trajectory optimization Safety

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Black-box domain adaptation for cross-domain on-device machinery fault diagnosis via hierarchical debiased self-supervised learning

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Knowledge-Based Systems 2025年 324卷

作者： Mengliang Zhu Jie Liu Yanglong Lu Zhongxu Hu Li Yuan Kaibo Zhou MOE Key Laboratory of Image Information Processing and Intelligent Control Belt and Road Joint Laboratory on Measurement and Control Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan 430074 China National Center of Technology Innovation for Digital Construction School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan 430074 China Department of Mechanical and Aerospace Engineering The Hong Kong University of Science and Technology Clear Water Bay Hong Kong SAR China School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China

This paper explores black-box domain adaptation (BBDA) for cross-domain on-device machinery fault diagnosis. Specifically, a pre-trained black-box source model is deployed on a cloud platform with only its input-output API accessible, and a randomly initialized target model is trained locally utilizing unlabeled target data on an edge device. In BBDA, neither raw data nor model parameters are transmitted across domains, and the only available supervised information for adaptation is API-queried predictions on unlabeled target data. However, API-queried predictions are inevitably noisy and class-imbalanced due to cross-domain distribution discrepancy and inherent category bias. Directly utilizing them for on-device training easily leads to confirmation bias and exacerbates category bias, thereby severely degrading performance. This paper proposes a hierarchical debiased self-supervised learning framework for BBDA from a novel research perspective of concurrently alleviating both confirmation and category biases. Specifically, debiased knowledge distillation is proposed to gradually enhance discriminability of the randomly initialized target model by providing debiased training predictions. An adaptive category-unbiased sample division strategy is devised to divide unlabeled target data into certain-aware and uncertain-aware sets, enabling fully exploiting intrinsic data structures and utilizing underlying sample relationships within the target domain. On this basis, debiased prototypical self-training is proposed for the certain-aware set to obtain reliable pseudo-labels for self-training, leveraging global semantic structure. Hierarchical neighborhood adaptation is customized for the uncertain-aware set to enforce the cluster assumption for noise refinement, exploiting local clustering structure and utilizing underlying sample relationships. Extensive experiments and analyses on public and practical datasets validate the proposed framework’s superiority in cross-dom

关键词： Black-box domain adaptation Category bias Confirmation bias Cross-domain on-device machinery fault diagnosis Self-supervised learning

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Deep Neural Network-Based Real-time Trajectory Planning for an Automatic Guided Vehicle with Obstacles

Deep Neural Network-Based Real-time Trajectory Planning for ...

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Chinese automation Congress (CAC)

作者： Jialun Lai Zhigang Ren Zongze Wu Yaqiang Liu Shengli Xie School of Automation Guangdong Key Laboratory of IoT Information Technology Guangdong University of Technology Guangzhou China Key Laboratory of Industrial Internet of Things & Networked Control Ministry of Education Chongqing University of Posts and Telecommunications Chongqing China School of Automation Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangdong University of Technology Guangzhou China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

In this paper, a hybrid-intelligent real-time optimal control approach based on deep neural networks (DNNs) for an automatic guided vehicle (AGV) with obstacles is studied. The path planning problem of the AGV is formulated as an optimal control problem (OCP) and the numerical solution of the OCP can be obtained by discretizing the OCP into a nonlinear programming (NLP) problem. However, due to the change of the starting point and the high nonlinearity of the model, the solution process is usually difficult without good initial guesses. Multiple iterations are often needed and real-time performance cannot be easily achieved. To this end, we first use the Gauss pseudospectral method incorporated a smoothing technique to solve the AGV optimal control problem with high computational efficiency offline. Subsequently, a DNNs is developed to learn the functional relationship between the optimal state and action pairs obtained by the approximate indirect method, and thus the resulting DNNs can generate the optimal control instructions in real time for the AGV navigation. Numerical results are illustrated to verify the real-time performance, control optimality and robustness of the developed DNN-based controller.

关键词： Optimal control Real-time systems Neural networks Trajectory planning Cost function Navigation Mathematical model

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An improved distributed nonlinear observer for leader-following consensus via differential geometry approach

arXiv

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arXiv 2020年

作者： Xu, Haotian Wang, Jingcheng Wang, Bohui Wang, Hongyuan Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 Autonomous Systems and Intelligent Control International Joint Research Center Xi'an Technological University Xi'an Shaanxi710021 China School of Aerospace Science and Technology Xidian University Xi'an710071 China

This paper is concerned with the leader-following output consensus problem in the framework of distributed nonlinear observers. In stead of certain hypotheses on the leader system, a group of geometric conditions is put forward to develop a novel distributed observer strategy with less conservatism, thereby definitely improving the applicability of the existing results. To be more specific, the improved distributed observer can precisely handle consensus problems for some nonlinear leader systems which are invalid for the traditional strategies with the certain assumption, such as Elastic Shaft Single Linkage Manipulator (ESSLM) systems and most of first-order nonlinear systems. We prove the sufficient conditions for the exponential stability of our distributed observer's error dynamic by proposing two pioneered lemmas to show the relationship between the maximum eigenvalues of two matrices appearing in Lyapunov type matrices. Then, a partial feedback linearization method with zero dynamic proposed in differential geometry is employed to design a purely decentralized control law for the affine nonlinear multiagent system. With this advancement, the existing results can be regarded as a specific case owing to that the followers can be chosen as an arbitrary minimum phase affine smooth nonlinear system. At last, the novel distributed observer and the improved purely decentralized control law are applied in the distributed control framework to construct a closed-loop system. We also prove the stability of closed-loop system to achieve leader-following consensus, i.e., the distributed control framework is proved to satisfy certainty equivalence principle. Our method is illustrated by ESSLM system and Van der Pol system as leader. Copyright © 2020, The Authors. All rights reserved.

关键词： Feedback linearization

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3D Hierarchical Refinement and Augmentation for Unsupervised Learning of Depth and Pose from Monocular Video

arXiv

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arXiv 2021年

作者： Wang, Guangming Zhong, Jiquan Zhao, Shijie Wu, Wenhua Liu, Zhe Wang, Hesheng Department of Automation Institute of Medical Robotics Key Laboratory of System Control and Information Processing 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 Department of Engineering Mechanics Shanghai Jiao Tong University Shanghai200240 China Department of Computer Science and Technology University of Cambridge CambridgeCB2 1TN United Kingdom

Depth and ego-motion estimations are essential for the localization and navigation of autonomous robots and autonomous driving. Recent studies make it possible to learn the per-pixel depth and ego-motion from the unlabeled monocular video. A novel unsupervised training framework is proposed with 3D hierarchical refinement and augmentation using explicit 3D geometry. In this framework, the depth and pose estimations are hierarchically and mutually coupled to refine the estimated pose layer by layer. The intermediate view image is proposed and synthesized by warping the pixels in an image with the estimated depth and coarse pose. Then, the residual pose transformation can be estimated from the new view image and the image of the adjacent frame to refine the coarse pose. The iterative refinement is implemented in a differentiable manner in this paper, making the whole framework optimized uniformly. Meanwhile, a new image augmentation method is proposed for the pose estimation by synthesizing a new view image, which creatively augments the pose in 3D space but gets a new augmented 2D image. The experiments on KITTI demonstrate that our depth estimation achieves state-of-the-art performance and even surpasses recent approaches that utilize other auxiliary tasks. Our visual odometry outperforms all recent unsupervised monocular learning-based methods and achieves competitive performance to the geometry-based method, ORB-SLAM2 with back-end optimization. Copyright © 2021, The Authors. All rights reserved.

关键词： Robots

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Fast-dynamic-vision: Detection and tracking dynamic objects with event and depth sensing

arXiv

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arXiv 2021年

作者： He, Botao Li, Haojia Wu, Siyuan Wang, Dong Zhang, Zhiwei Dong, Qianli Xu, Chao Gao, Fei State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China Huzhou Institute of Zhejiang University Huzhou313000 China Ningbo315000 China School of Automation Nanjing Institute of Technology Nanjing211112 China Faculty of Robot Science and Engineering Northeastern University Shenyang110207 China Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’an710049 China

The development of aerial autonomy has enabled aerial robots to fly agilely in complex environments. However, dodging fast-moving objects in flight remains a challenge, limiting the further application of unmanned aerial vehicles (UAVs). The bottleneck of solving this problem is the accurate perception of rapid dynamic objects. Recently, event cameras have shown great potential in solving this problem. This paper presents a complete perception system including ego-motion compensation, object detection, and trajectory prediction for fast-moving dynamic objects with low latency and high precision. Firstly, we propose an accurate ego-motion compensation algorithm by considering both rotational and translational motion for more robust object detection. Then, for dynamic object detection, an event camera-based efficient regression algorithm is designed. Finally, we propose an optimization-based approach that asynchronously fuses event and depth cameras for trajectory prediction. Extensive real-world experiments and benchmarks are performed to validate our framework. Moreover, our code will be released to benefit related researches. © 2021, CC BY-NC-ND.

关键词： Object recognition

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Arbitrary state transition of open qubit system based on switching control

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Automatica 2025年 179卷

作者： Guangpu Wu Shibei Xue Shan Ma Sen Kuang Daoyi Dong Ian R. Petersen Department of Automation Shanghai Jiao Tong University Shanghai 200240 PR China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 PR China School of Automation Central South University Changsha 410083 PR China Department of Automation University of Science and Technology of China Hefei 230027 PR China Australian Artificial Intelligence Institute Faculty of Engineering and IT University of Technology Sydney Ultimo 2007 NSW Australia School of Engineering Australian National University Canberra ACT 2601 Australia

We present a switching control strategy based on Lyapunov control for arbitrary state transitions in open qubit systems. This strategy can prevent the state from entering invariant or singularity sets, effectively driving the system ultimately to a sufficiently small neighborhood of target states, which relaxes strict constraints on system models imposed by special target states compared to existing works. For the arbitrary transition, practical finite-time stability (PFTS) and its contractive version (PFTCS) conditions are given, respectively, where asymptotic stability is unattainable in open quantum systems in general. The efficacy of our method is demonstrated in examples of a qubit affected by amplitude damping, dephasing, and depolarizing decoherence.

关键词：

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Parity space-based model mismatch detection for linear discrete time-invariant systems with unknown disturbances

Parity space-based model mismatch detection for linear discr...

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第三十九届中国控制会议

作者： Yi Tang Dan Ling Hong Zhang Yanewei Wang Ying Zheng the Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and Technology School of Electrical and Information Engineering Zhengzhou University of Light Industry School of Mechanical & Electrical Engineering Wuhan Institute of Technology

Model mismatch is one of the main factors of control performance degradation. In this paper, a new model mismatch detection approach with parity space-based methods is proposed for linear discrete time-invariant（LDTI） systems. This paper establishes a parity space-based residual evaluation function for the detection of model mismatch. The problem of parity space-based model mismatch is formulated on a state space model of a LDTI system. A standard evaluation function and its thresholds for the detection of model mismatch are proposed by providing the parity model-based residuals. It is shown the proposed approach results in an optimal fault detectability without any assumptions on the probability distribution of unknown disturbances. The effectiveness of the proposed methods is demonstrated by the simulation study of the paper machine head box control example.

关键词： Parity space Model mismatch detection LDTV systems model-based residual generation

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The Performance Evaluation and Resilience Analysis of Supply Chain Based on Logistics Network

The Performance Evaluation and Resilience Analysis of Supply...

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Chinese control Conference (CCC)

作者： Weiqi Sun Yuanlong Li Liangren Shi Department of Automation Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing of the Ministry of Education Shanghai P. R. China Shanghai Key Laboratory of Navigation and Location-based Services and Academy of Information Technology and Electrical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

With the development of globalization, more and more enterprises are involved in the supply chain network with increasingly complex structure. In this paper, enterprises and relations in the logistics network are abstracted as nodes and edges of the complex network. A graph model for a supply chain network to specified industry is constructed, and the Neo4j graph database is employed to store the graph data. This paper uses the theoretical research tool of complex network to model and analyze the supply chain, and designs a supply chain network evaluation system which include static and dynamic measurement indexes according to the statistical characteristics of complex network. In this paper both the static and dynamic resilience characteristics of the the constructed supply chain network are evaluated from the perspective of complex network. The numeric experimental simulations are conducted for validation. This research has practical and theoretical significance for enterprises to make strategies to improve the anti-risk capability of supply chain network based on logistics network information.

关键词： Supply chains Complex networks Indexes Robustness Resilience

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