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14th IEEE Global Reliability and Prognostics and Health Management Conference, PHM-Hangzhou 2023

作者： Tao, Sen Wang, Kai Zeng, Peng Yu, Tianda Wu, Hao Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110169 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110169 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Nuclear Power Institute of China Chengdu610213 China State Key Laboratory of Power Transmission Equipment & System Security and New Technology Chongqing University Chongqing400044 China

ISBN: (纸本)9798350301359

Industrial equipment reliability is a critical issue that smart manufacturing systems must address. Implementation of online fault diagnosis for industrial equipment is a promising technology that can improve equipment reliability. Vibration analysis is a popular technique for equipment condition monitoring. For the non-linear and non-stationary features of industrial vibration signals, a hybrid model based on wavelet transform (WT), support vector machine (SVM), and grey wolf optimization algorithm (GWO) is proposed. The time-frequency domain features of fault signals are extracted by wavelet transform to solve the non-linearity of signals, and the obtained feature vectors are input into SVM for classification. Aiming at the problem of setting penalty coefficient and kernel function parameters, GWO is introduced to seek hyperparameters by using the hunting principle of wolves. In the experimental part, firstly, the comparison of the GWO-WT-SVM, GWO-SVM and SVM models was carried out. Secondly, Comparison with classical classification algorithms is made. Apparently, the GWO-WT-SVM model achieves higher accuracy than other models in bearing fault diagnosis. © 2023 IEEE.

关键词： component Fault diagnosis GWO PHM SVM

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Cooperative Diving of Multi-AUV system with Self-triggered Mechanism

Cooperative Diving of Multi-AUV System with Self-triggered M...

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2022 IEEE International Conference on Unmanned systems, ICUS 2022

作者： Zhang, Junjie Yang, Jingxin Bian, Yougang Cui, Qingjia Qin, Xiaohui Hu, Manjiang Shen, Da Gao, Hongbo Hunan University State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Changsha China Institute of Hunan University Wuxi Intelligent Control Research Wuxi China University of Science and Technology of China The Department of Automation Hefei China

ISBN: (数字)9781665484565

ISBN: (纸本)9781665484565

A self-triggered distributed model predictive control (DMPC) approach is proposed in this paper for the cooperative diving of a multi-autonomous underwater vehicle (AUV) system. In order to realize cooperative diving of the multi-AUV system, we design a basic DMPC mechanism with a stability condition which is easy to be realized. Then, a self-triggering mechanism is introduced into the basic DMPC approach which saves communication and computation resources. In the self-triggered mechanism, the maximum time instant that is inside the terminal region decides the triggering interval. The triggering interval is decided after solving the optimization problem, therefore helps reduce the computation burden at each control loop. Lastly, numerical examples are provided to verify the effectiveness of the proposed method. © 2022 IEEE.

关键词： Autonomous underwater vehicles

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Data-Driven Learning and Control with Event-Triggered Measurements

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IEEE Transactions on Automatic Control 2025年

作者： Feng, Shilun Shi, Dawei Chen, Tongwen Shi, Ling Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems MIIT Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing100081 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Clear Water Bay Kowloon Hong Kong

Event-triggered control has attracted considerable attention for its effectiveness in resource-restricted applications. To make event-triggered control as an end-to-end solution, a key issue is how to effectively learn unknown system dynamics from event-triggered measurements and consequently, develop a learning-based event-triggered controller. Existing works learn system dynamics based on periodic time-triggered measurements, and it is yet to know how to learn a controller with performance guarantee based on event-triggered measurements. To address this issue, we consider the problem of learning an event-triggered state feedback controller for an unknown linear system based on event-triggered state measurements in this work. In particular, we first analyze the event-triggered measurements within a set-membership framework. We prove that the estimation error belongs to a bounded ellipsoid determined by the historical measurements and the event-triggering condition. Subsequently, we demonstrate that all admissible systems compatible with the collected data samples can be explicitly represented in the form of quadratic matrix inequalities using the state estimates. With the acquired set of admissible systems, a co-design problem for the data-driven controller and event-triggering condition is solved using the linear matrix inequality technique, with guaranteed closed-loop stability and L2-gain performance. Finally, numerical examples and comparisons are provided to illustrate the effectiveness of the proposed event-triggered learning and control approach. © 1963-2012 IEEE.

关键词： Networked control systems

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Constrained Probabilistic Pareto Dominance for Expensive Constrained Multiobjective Optimization Problems

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Zhang, Zhiyao Wang, Yong Sun, Guangyong Pang, Tong Tang, Ke School of Automation Central South University Changsha China State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle Hunan University Changsha China Guangdong Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering and the Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China

This paper proposes a new parameterless constraint-handling technique, named constrained probabilistic Pareto dominance (CPPD), for expensive constrained multiobjective optimization problems (CMOPs). In CPPD, when comparing two solutions, in terms of each original objective, we design a new objective for each solution, which is the negative product of two probabilities calculated based on the predicted fitness mean values and the uncertainty information provided by Kriging models: 1) the probability that this solution satisfies all constraints, denoted as PoF, and 2) the probability that this solution is better than the other on the original objective, denoted as PoB. It is evident that for each solution, PoF and PoB indicate its feasibility and its optimality on the corresponding original objective, respectively. Then, Pareto dominance based on new objectives is executed. As a result, both competitive feasible solutions and promising infeasible solutions with good diversity can be preserved by CPPD. These two kinds of solutions can help the population to exploit the located feasible parts and to explore new feasible parts, respectively. Further, based on CPPD, we develop a Pareto-based Kriging-assisted constrained multiobjective evolutionary algorithm (called PEA) to deal with expensive CMOPs with two or three objectives. Finally, PEA is generalized to solve expensive constrained many-objective optimization problems, named PEA+. The effectiveness of CPPD, PEA, and PEA+ is verified by comprehensive experiments. IEEE

关键词： Evolutionary algorithms

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Early Forest Fire Recognition Method Based on C-GhostNet Network 5

Early Forest Fire Recognition Method Based on C-GhostNet Net...

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5th International Symposium on Autonomous systems, ISAS 2022

作者： Li, Mengna Zhang, Youmin Mu, Lingxia Xin, Jing Yu, Ziquan Jiao, Shangbin Liu, Han Xie, Guo Yi, Yingmin Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Shaanxi Xi'an710048 China Concordia University Industrial and Aerospace Engineering Department of Mechanical MontrealQCH3G 1M8 Canada Nanjing University of Aeronautics and Astronautics College of Automation Engineering Nanjing211106 China Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an Shaanxi 710048 China

ISBN: (纸本)9781665487085

Early forest fire image recognition plays an important role in timely fire fighting. This paper proposes an early forest fire recognition method based on C-GhostNet network. The C-GhostNet network is an improved version of GhostNet, which mainly consists of a series of c-ghost bottlenecks, each c-ghost bottleneck mainly consists of two c-ghost modules. Through the concat operation and channel shuffle, the c-ghost module establishes the relationship between the input data and the output features generated by the original ghost module, so that GhostNet network has the ability to capture contextual features. The experimental results show that the C-GhostNet network achieves better recognition performance than GhostNet without introducing additional computational load, and the running speed reaches about 20FPS, which basically realizes real-time recognition. In addition, like GhostN et, the C-GhostNet network is also a lightweight network. © 2022 IEEE.

关键词： Image recognition

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Extended NNs-Based Adaptive Event-Triggered Asymptotic Control for Nonlinear systems Against Actuator Faults

Extended NNs-Based Adaptive Event-Triggered Asymptotic Contr...

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International Symposium on Autonomous systems (ISAS)

作者： Shanlin Liu Yingwei Zhang Xudong Zhao Hesheng Wang College of Information Science and Engineering Northeastern University Shenyang China State Laboratory of Synthesis Automation of Process Industry Northeastern University Shenyang China Faculty of Electronic Information and Electrical Engineering Dalian University of Technology Dalian 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 IntelligentControl and Management Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350363173

ISBN: (纸本)9798350363180

In this article, an adaptive event-triggered asymp¬totically fault-tolerant control (FTC) issue for nonlinear systems with actuator faults is investigated. An extended neural networks (NNs) technique is introduced to estimate unknown functions of the system under consideration. Compared with traditional NNs that ignore approximation errors, the proposed extended NNs further processes approximation errors, reducing the conservatism of the proposed algorithm. Then, within the framework of backstepping method, a new adaptive control strategy is developed by combining event-triggered mechanism (ETM) and bound estimation (BE) approach, which ensures the asymptotic convergence of system states while also reducing the transmission of unnecessary information, thus improving the utilization of communication resources. At the same time, the designed controller eliminates the adverse effects of actuator failures and system nonlinear terms. Finally, a simulation example of a single-link robot system is provided to demonstrate that the developed control strategy can ensure that all signals are bounded and system states are asymptotically convergent.

关键词： Actuators Backstepping Fault tolerant systems Process control Artificial neural networks Control systems Approximation error

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6DoF-ICTC: A Deep Image Feature Extraction Network Combining Transformers and CNNs for 6DoF Pose Estimation 12

6DoF-ICTC: A Deep Image Feature Extraction Network Combining...

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12th International Conference on CYBER technology in automation, Control, and intelligent systems, CYBER 2022

作者： Zhang, Liming Zhou, Xin Zhu, Baixian Wang, Can Wu, Xinyu Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology China University of Science and Technology of China China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

In this work, we propose a new image feature extraction network for 6D pose estimation tasks. The majority of existing 6D pose estimation networks are based on the RGBD input, which is the fusion of images and point clouds. They are mainly working on keypoint extraction, point cloud feature extraction, and other tasks, however, image features, on the other hand, mainly rely on CNN networks that are currently advanced. With the development of Vision Transformer in the field of computer vision, the results on ImageNet demonstrate its benefit of extracting global features, and we see new directions that the Transformers can bring to the area of image feature extraction. ICTC is a Transformer paired with a CNN network that fuses the retrieved global features into each pixel and then does local feature extraction. This provides greater robustness to 6D pose estimation In complex situations such as shifting lighting conditions and object occlusion. Our method achieves 96% accuracy using the average ADD metric on the LineMOD dataset, demonstrating our method's effectiveness. © 2022 IEEE.

关键词： Feature extraction

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Bifurcation variation of a novel line-symmetric double-centered 6R metamorphic mechanism based on ellipses

Bifurcation variation of a novel line-symmetric double-cente...

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2023 IEEE International Conference on Robotics and Biomimetics, ROBIO 2023

作者： Lin, Song Song, Yifeng Wang, Hongguang Yuan, Bingbing Dai, Jiansheng Jing, Fengren Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics State Key Laboratory of Robotics Shenyang110016 China Chinese Academy of Sciences Intelligent Manufacturing Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Southern University of Science and Technology Department of Mechanical and Energy Engineering Shenzhen518055 China

ISBN: (纸本)9798350325706

This paper proposed a line-symmetric double-centered 6R metamorphic mechanism with bifurcation properties. The mechanism is constructed based on elliptical geometric properties, which is a comprehensive design method for constructing novel metamorphic mechanisms through intuitive geometric figures. Firstly, the geometric constraints of the 6R metamorphic mechanism are revealed, and the parameter constraints and closed-loop kinematic equations of the 6R metamorphic mechanism are deduced. Through the analysis of mechanism geometric constraints, four motion branches of the 6R metamorphic mechanism are obtained: collinear deployable motion branch MB1, spherical 4R motion branch MB2, spherical 4R motion branch MB3, and line-symmetrical Bricard motion branch MB4. Meanwhile, according to the screw theory, the analytical expressions and mobility of the constraint-screw systems under different motion branches are deduced. This led to the identification of the constraint and motion branch variations of the 6R metamorphic mechanism. Finally, the four serial link 4R singular configurations of the 6R metamorphic mechanism are specially studied, and the characteristics of the mutual reconfiguration of these four singular configurations are revealed. © 2023 IEEE.

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Road Surface Condition Recognition Based on Visual Information Fusion

Road Surface Condition Recognition Based on Visual Informati...

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2024 China automation Congress, CAC 2024

作者： Zhu, Qingyuan Wei, Tao Zhou, Yijie Tan, Kai Gao, Yunlong Shao, Guifang Li, Zhichao College of Transportation and Navigation Quanzhou Normal University Pen-Tung Sah Institute of Micro-Nano Science and Technology Xiamen University Xiamen China State Key Laboratory of Intelligent Agricultural Power Equipment Henan China School of Electronic Science and Engineering Xiamen University Xiamen China Department of Automation Xiamen University Xiamen China Pen-Tung Sah Institute of Micro-Nano Science and Technology Xiamen University Xiamen China

ISBN: (纸本)9798350368604

To address the problem of lower classification accuracy in current road surface condition recognition methods for both daytime and nighttime, this paper proposes an end-to-end deep learning model. Which integrates RGB and Near Infrared (NIR) dual-channel visual information for autonomous road surface condition classification. The collected image data is first augmented to mitigate the effects of model overfitting. A lightweight network based on ConvNeXt-Tiny is designed to extract basic features from RGB and NIR images, respectively. A feature fusion module based on Feature Pyramid Network (FPN) is designed to fuse hierarchical features, which are then classified together with the basic features using fully connected layers. Comparative experiments with MBFN and ablative studies demonstrate the effectiveness of the proposed network model and feature fusion module. © 2024 IEEE.

关键词： Deep learning

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Characterization of plasma discharge channel in EDM using photography and spectroscopy 15

Characterization of plasma discharge channel in EDM using ph...

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15th CIRP Conference on intelligent Computation in Manufacturing Engineering, CIRP ICME 2021

作者： Qin, Ling Liu, Chen Zhang, Ruixue Zhang, Ya-Ou Yang, Xiaodong Zhao, Wansheng State Key Laboratory of Mechanical System and Vibration China School of Mechanical Engineering Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China Department of Mechanical Engineering and Automation Harbin Institute of Technology Harbin150001 China

The plasma discharge channel of electrical discharge machining (EDM) brings a temperature of several thousands of degrees Celsius into the gap between the anode and cathode, resulting in the material removal by means of melting and evaporating. To investigate the characteristics of the plasma discharge channel, high-speed imaging technology is adopted to observe the dimension of the plasma. A spectrometer is used to calibrate the temperature of the plasma as well. Then the particle-in-cell with Monte Carlo collision (PIC-MCC) simulation is conducted to analyze the variation and distribution of the plasma and heat flux. The experimental results demonstrate that the discharge channels' morphology obtained is stable after a few microseconds of expansion after breakdown, and the diameter of discharge channel and the current density increases with the increase of discharge current. Besides, the emission intensity of plasma increases with the increase of electrical parameters. However, the temperature of plasma decreases after high current, which means debris particles make machining condition deteriorated. Finally, the simulation results demonstrate that despite the current density increases, the maximum heat flux at the center of electrode reduces rapidly with the expansion of the discharge channel. The distribution of heat flux upon electrode evolves towards "wider and flatter", leading to larger debris particles than exceptional size. © 2022 The Author(s).

关键词： High speed photography

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