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2021 International Conference on Unmanned Aircraft systems, ICUAS 2021

作者： Cheng, Zihuan Pei, Hailong College of Automation Science and Engineering South China University of Technology Guangzhou510640 China Key Laboratory of Autonomous Systems and Networked Control Unmanned Aerial Vehicle Systems Engineering Technology Research Center of Guangdong College of Automation Science and Engineering South China University of Technology Ministry of Education Guangzhou510640 China

ISBN: (纸本)9780738131153

In this paper, we focus on the hover/high-speed-cruising transition flight control of a ducted fan unmanned aerial vehicle (UAV), in which saturation on control surfaces is taken into consideration. To achieve full-envelope flight control, we propose an adaptive cascaded structure on the velocity tracking control for the nonlinear aircraft system. By introducing adaptive neural network, the closed-loop system tracks a prescribed reference system with all the nonlinear tracking errors well canceled. In the meanwhile, to tackle the control surface saturation, we propose a control effectiveness enhancement (CEE) algorithm in attaining sufficient control moment to the ducted fan vehicle. Under the proposed cascaded adaptive control scheme in conjunction with the CEE algorithm, the state tracking performance is proved to be bounded when encountering surface saturation. Finally, comparison experiments in terms of the forward and backward flight transition are conducted on the ducted fan UAV. The results are satisfactory and convincing in exhibiting the flight transition course that demonstrates the effectiveness of the proposed method. © 2021 IEEE.

关键词： Closed loop systems

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A reasoning diagram based method for fault diagnosis of railway point system

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High-Speed Railway 2023年第2期1卷 110-119页

作者： Feng Wang Yuan Cao Clive Roberts Tao Wen Lei Tan Shuai Su Tao Tang State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong UniversityBeijing 100044China National Engineering Research Center of Rail Transportation Operation and Control System School of Electronic and Information EngineeringBeijing Jiaotong UniversityBeijing100044China Birmingham Centre for Railway Research and Education University of BirminghamBirmingham B152TTUK School of Electronic and Information Engineering Beijing Jiaotong UniversitBeijing 100044China Fronters Science Center or Smart High-speed Raiway Systm Beijing Jiotong Univers Beijing 00044China

Railway Point system(RPS)is an important infrastructure in railway industry and its faults may have significant impacts on the safety and efficiency of train *** the fault diagnosis of RPS,most existing methods assume that sufficient samples of each failure mode are available,which may be unrealistic,especially for those modes of low occurrence frequency but with high *** address this issue,this work proposes a novel fault diagnosis method that only requires the power signals generated under normal RPS operations in the training ***,the failure modes of RPS are distinguished through constructing a reasoning diagram,whose nodes are either binary logic problems or those that can be decomposed into the problems of the binary ***,an unsupervised method for the signal segmentation and a fault detection method are combined to make decisions for each binary logic *** on the results of decisions,the diagnostic rules are established to identify the failure ***,the data collected from multiple real-world RPSs are used for validation and the results demonstrate that the proposed method outperforms the benchmark in identifying the faults of RPSs.

关键词： Railway point system Fault diagnosis Reasoning diagram Segmentation Detection method

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Real-time Double-layer index Structure for Massive Point Cloud Data

Real-time Double-layer index Structure for Massive Point Clo...

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2020 International Symposium on autonomous systems, ISAS 2020

作者： Ou, Chenxi Pei, Hailong Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Unmanned Aerial Vehicle Systems Engineering Technology Research Center of Guangdong South China University of Technology Guangzhou China

ISBN: (纸本)9781665418829

To handle the problems of point cloud data quantity explosion and urgent need for real-time access to massive point cloud data in collecting and storing phase, this paper proposes an index technology with internal and external storage interactions, which is based on extended 3D grid in memory layer and B-tree in external storage layer. the experiment shows that it is efficient to retrieve massive point cloud data by using this double-layer index structure. © 2020 IEEE.

关键词： Trees (mathematics)

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HRTS: Hierarchical Rauch-Tung-Striebel Smoother With Online Learning Priors for EEG Denoising

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第2期9卷 673-688页

作者： Wang, Chuan-Sheng Zhang, Ling Fu, Tian-Lin Chen, Zhao-Qi Zhang, Fu-Quan Department of Automatic Control Technical Polytechnic University of Catalonia Autonomous Region of Catalonia Barcelona Spain School of Computer and Data Science Minjiang University No.200 Xiyuangong Road Fuzhou University Town Fujian Province Fuzhou City China College of Computer and Big Data Fuzhou University No.2 Wulongjiang North Road Fuzhou University Town Fujian Province Fuzhou City China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Fuzhou Technology Innovation Center of Intelligent Manufacturing Information System Minjiang University No.200 Xiyuangong Road Fuzhou University Town Fujian Province Fuzhou City China Fujian Province University No.1 Campus New Village Longjiang Street Fujian Province Fuqing City China

Electroencephalogram (EEG) is a nonlinear signal that reflects the physio-logical state of the brain at different times, containing rich information. However, the possible interference during the collection and transmission process often leads to a large amount of unnecessary noise in the EEG signal. Traditional denoising methods may face difficulties in high-dimensional data processing and the inability to effectively handle different types of Gaussian white noise. To better eliminate the interference of Gaussian white noise, this article specifically adopts the Hierarchical Rauch-Tung-Striebel Smoother (HRTS) method. This method can effectively integrate the learned structural prior EEG signals into the state space model, thereby describing the relationship between EEG signals and noise. Capture the spatiotemporal characteristics of EEG signals through hierarchical modeling, and optimize the components of EEG signals through estimation and prediction of hidden variables. Finally, the mean square error (MSE) is used as an evaluation indicator to compare and evaluate the denoising results. Empirical research has shown that the HRTS algorithm can not only effectively reduce runtime and better process high-dimensional data, but also significantly improve the quality of EEG signals, effectively suppress noise interference, and more accurately reflect the characteristics of brain activity. Compared to denoising algorithms such as Kalman filtering and Kalman wavelet filtering, the HRTS algorithm has more advantages in denoising EEG signals. © 2024, J. Network Intell. All rigjts reserved.

关键词： Electroencephalography

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Study on High-Speed-to-Hovering Back-Transition control of Ducted Fan UAV

Study on High-Speed-to-Hovering Back-Transition Control of D...

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第33届中国控制与决策会议

作者： Feng Chen Hailong Pei Zihuan Cheng Key Laboratory of Autonomous Systems and Networked Control Ministry of EducationUnmanned Aerial Vehicle Systems Engineering Technology Research Center of GuangdongSouth China University of Technology

The ducted fan unmanned aerial vehicle(UAV) can transit from hovering to high-speed flight by continuously pitching down,reaching a high-speed and high-efficiency ***,during the back-transition process from high speed to hovering,unstable environmental airflow and improper velocity planning could lead to serious oscillation,or even divergence of the aircraft *** this paper,we address this control problem of a ducted fan UAV by utilizing an expected velocity planning method and a robust *** expected velocity curve is planned to ensure the dynamic effect of the deceleration process and decrease the computational complexity in solving the numerical optimization *** robust controller aims to reduce the tracking error of velocity and angular velocity considering modeling uncertainty and ***,the unit quaternion is selected to represent the attitude to avoid the singularity *** analysis verifies the stability of the proposed controller based on unit ***,the simulation and experimental results collectively indicate the effectiveness of our methods.

关键词： Ducted Fan UAV Back-Transition Velocity Planning Robust control Unit Quaternion

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Sensor-based Multi-Robot Coverage control with Spatial Separation in Unstructured Environments

Sensor-based Multi-Robot Coverage Control with Spatial Separ...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Xinyi Wang Jiwen Xu Chuanxiang Gao Yizhou Chen Jihan Zhang Chenggang Wang Yulong Ding Ben M. Chen Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Shatin N.T. Hong Kong China Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai P. R. China Department of Control Science and Engineering Tongji University Shanghai China Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Multi-robot systems have increasingly become instrumental in tackling coverage problems. However, the challenge of optimizing task efficiency without compromising task success still persists, particularly in expansive, unstructured scenarios with dense obstacles. This paper presents an innovative, decentralized Voronoi-based coverage control approach to reactively navigate these complexities while guaranteeing safety. This approach leverages the active sensing capabilities of multi-robot systems to supplement GIS (Geographic Information system), offering a more comprehensive and real-time understanding of environments like post-disaster. Based on point cloud data, which is inherently non-convex and unstructured, this method efficiently generates collision-free Voronoi regions using only local sensing information through spatial decomposition and spherical mirroring techniques. Then, deadlock-aware guided map integrated with a gradient-optimized, centroid Voronoi-based coverage control policy, is constructed to improve efficiency by avoiding exhaustive searches and local sensing pitfalls. The effectiveness of our algorithm has been validated through extensive numerical simulations in high-fidelity environments, demonstrating significant improvements in task success rate, coverage ratio, and task execution time compared with others.

关键词： Point cloud compression system recovery Robot sensing systems Sensors Safety Multi-robot systems Task analysis

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AGI in Metaverse for Smart Cities and Societies: A Cyber Physical Social Approach

AGI in Metaverse for Smart Cities and Societies: A Cyber Phy...

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control Conference (ANZCC), Australian and New Zealand

作者： Xiao Wang Jun Huang Yong-Lin Tian Fei-Yue Wang Engineering Research Center of Autonomous Unmanned System Technology Ministry of Education Anhui University Hefei China Faculty of Innovation Engineering Macau University of Science and Technology Macao China State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Beijing China Macao Institute of Systems Engineering Macau University of Science and Technology Macao China

The development of artificial general intelligence (AGI) and metaverse technologies hold great potential to enhance the effectiveness of smart cities and societies. we present a cyberphysical-social approach to integrating AGI into metaverse-based smart cities and societies, with a specific focus on parallel intelligence. The proposed approach considers the interplay between the physical environment, digital systems, social dynamics, and emerging forms of parallel intelligence, as well as swarm intelligence and collective intelligence. By leveraging these forms of parallel intelligence, the integration of AGI can create more responsive and proactive infrastructure, personalized and efficient services, and foster greater community engagement. The metaverse acts as a platform for simulating real-world scenarios and testing and refining AGI models before deployment in the physical world, while also facilitating the development and coordination of parallel intelligence. However, ethical considerations must be taken into account to ensure responsible deployment and avoid exacerbating existing societal inequalities or infringing upon individual privacy rights. This report provides a comprehensive framework for the integration of AGI and parallel intelligence into metaverse-based digital and smart organizations, while emphasizing the importance of human ethics, social responsibility, and ecological sustainability.

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A Novel Hybrid Method for Fault Diagnosis of Industrial Equipment Based on Vibration Signals

A Novel Hybrid Method for Fault Diagnosis of Industrial Equi...

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Prognostics and system Health Management Conference (PHM-Qingdao)

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

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.

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Recurrent Attentional Reinforcement Learning for Fault Diagnosis of Hydraulic system

Recurrent Attentional Reinforcement Learning for Fault Diagn...

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International Conference on control, Decision and Information Technologies (CoDIT)

作者： Zhenhui Tang Ru Liu Kang Lou Chaojian Gao Jingcheng Wang SJTU Sanya Yazhou Bay Institute of Deepsea Science and Technology Sanya China Department of Automation Shanghai Jiao Tong University Shanghai China Shanghai Marine Equipment Research Institute Shanghai China Department of Automation the Key Laboratory of System Control and Information Processing Ministry of Education of China and the Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China Autonomous Systems and Intelligent Control International Joint Research Center Xi'an Technological University Xi'an China

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

Recognizing fault types of machinery system is a fundamental but challenging task in industrial application. Although data-driven fault diagnosis method attains remarkable progress by learning fault features automatically, the existing data-driven fault diagnosis pipelines learn non-discriminative feature. To deal with these problems, a new data-driven fault diagnosis method with recurrent attentional module is proposed to find a sequence of informative features by localizing distinguishing time fragments about each individual faults iteratively. The contextual sequence dependency between the localized time fragments is enhanced. Then, the fault types are further predicted on localized time fragments. The proposed framework also explicitly models long-term dependencies among these attentional time fragments to capture historical fault information. Experiments on hydraulic system show that the proposed framework achieves superior diagnosis performance and computation efficiency.

关键词： Fault diagnosis Location awareness Accuracy Computational modeling Pipelines Reinforcement learning Hydraulic systems Computational efficiency Information technology

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Design and Implementation of a Group control Communication system for Intelligent Rubber Tapping Robots

Design and Implementation of a Group Control Communication S...

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Chinese control Conference (CCC)

作者： Fuchun Liu Dong Jiang Xiaoyang Chen Xujian Chen Shengfeng Huang Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Unmanned Aerial Vehicle Systems Engineering Technology Research Center of Guangdong School of Automation Science and Engineering South China University of Technology Guangzhou P. R. China Beijing Normal University Hong Kong Baptist United International College ZhuHai P. R. China

With the development and promotion of electric rubber tapping equipment, China has gradually mechanized rubber tapping, but the degree of automation could be higher. This paper designs an intelligent rubber tapping remote monitoring system based on Internet of Things(IoT) technology that is applied to single-motor driven rubber tapping robots and uploads the status data of robots in the rubber plantation to the cloud database for storage, data analysis, and display in the backend management system in order to realize the intelligent and digital upgrade of rubber tapping robots. It can achieve remote group control of the robots in the rubber plantation at the same time using a cloud-based human-machine interface. The system consists of a terminal electronic control subsystem, a communication subsystem, and a cloud-based backend management system. This system uses LoRa RF technology and Narrowband IoT(NBIoT) technology to design a star-shaped network to connect the robots to the cloud for unified management and provide data support for later tapping decisions.

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