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7th International Symposium on Autonomous systems, ISAS 2024

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

ISBN: (纸本)9798350363173

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. © 2024 IEEE.

关键词： Approximation algorithms

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Machine Learning for Signal Demodulation in Underwater Wireless Optical Communications

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China Communications 2024年第5期21卷 297-313页

作者： Ma Shuai Yang Lei Ding Wanying Li Hang Zhang Zhongdan Xu Jing Li Zongyan Xu Gang Li Shiyin Department of Strategic and Advanced Interdisciplinary Research Peng Cheng LaboratoryShenzhen 518055China School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China Data-driven Intelligent Information System Laboratory Shenzhen Research Institute of Big DataShenzhen 518172China Ocean College Zhejiang UniversityZhoushan 316021China State Key Laboratory of Millimeter Waves Southeast UniversityNanjing 210096China

The underwater wireless optical communication(UWOC)system has gradually become essential to underwater wireless communication *** other existing works on UWOC systems,this paper evaluates the proposed machine learningbased signal demodulation methods through the selfbuilt experimental *** on such a platform,we first construct a real signal dataset with ten modulation ***,we propose a deep belief network(DBN)-based demodulator for feature extraction and multi-class feature *** also design an adaptive boosting(Ada Boost)demodulator as an alternative scheme without feature filtering for multiple modulated ***,it is demonstrated by extensive experimental results that the Ada Boost demodulator significantly outperforms the other *** also reveals that the demodulator accuracy decreases as the modulation order increases for a fixed received optical power.A higher-order modulation may achieve a higher effective transmission rate when the signal-to-noise ratio(SNR)is higher.

关键词： AdaBoost DBN machine learning signal demodulation

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Structure design and analysis of a rigid-soft robotic hand with large load capacity 3

Structure design and analysis of a rigid-soft robotic hand w...

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3rd International Conference on Electrical, Electronics, and Information Engineering, EEIE 2024

作者： Wu, Peichen Lan, Fuming Hong, Wen Chen, Xiaoping Shuai, Wei Cui, Guowei Gao, Yang Institute of Intelligent Manufacturing Guangdong Academy of Sciences Guangdong Key Laboratory of Modern Control Technology Guangzhou510070 China Shanghai Institute of Aerospace System Engineering Shanghai201100 China Intelligent Manufacturing Institute of HFUT HeFei230041 China School of Computer Science and Technology University of Science and Technology of China Hefei230026 China

ISBN: (纸本)9781510688179

In recent years, soft grippers have gained significant attention due to their adaptability and safety. However, when performing horizontal object grasping, their limited structural stiffness prevents them from withstanding the torque generated by friction between the gripper and the object, leading to a loss of contact and limiting their horizontal load capacity. To address this limitation while preserving the adaptability of soft grippers, this paper proposes a gripper structure called the rigid-soft robotic hand, which incorporates lateral rigid connecting rods outside the soft joints. The paper analyzes the bending capacity of the rigid-soft joint using kinematic methods and investigates the influence of link design parameters on maximum bending angle. Subsequently, a finite element analysis is conducted to study the finger structure with the added connecting rods, aiming to optimize the link design parameters. Experimental results demonstrate the grasping capabilities of the rigid-soft gripper in both vertical and horizontal orientations. The findings show that the rigid-soft gripper significantly improves the load-carrying capacity during horizontal object grasping while maintaining the adaptability of the soft gripper. © The Authors.

关键词： Robotic arms

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A Generalized Wind Turbine Anomaly Detection Method Based on Combined Probability Estimation Model

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Journal of Modern Power systems and Clean Energy 2023年第4期11卷 1136-1148页

作者： Xiangjun Zeng Ming Yang Chen Feng Yaohua Tang Key Laboratory of Power System Intelligent Dispatch and Control Shandong UniversityJinan 250061Chinaand X.Zeng is also with the College of Electrical Engineering&New EnergyChina Three Gorges UniversityYichang 443002China Rundian Energy Science and Technology Co. Ltd.Zhengzhou 450046China

Anomaly detection based on the data collected from the supervisory control and data acquisition(SCADA)system is crucial to reduce the failure rate of wind turbines(WTs).The difficulty of this kind of methods is to dynamically identify the threshold for anomaly detection under changing operating *** this paper,a generalized WT anomaly detection method based on the combined probability estimation model(CPEM)is *** CPEM can estimate the conditional probability density function(PDF)of the target variable given changing *** generalization and accuracy are better than those of the independent probability estimation model because it combines the advantages of various kinds of probability estimation models through linear *** using the CPEM,the normal operating bounds under different operating conditions can be obtained,which dynamically form the thresholds for anomaly ***,with respect to the thresholds,hypothesis testing(HT)is adopted to identify the anomaly by inspecting whether the observations exceed the thresholds at a given significance level,providing sound mathematical support for anomaly detection and making the detection results more *** effectiveness of the proposed method is tested by using the actual data of WTs with known *** results illustrate that the proposed method can detect the abnormal operating state of the gearbox and generator much more early than the system fault alarm.

关键词： Anomaly detection combined probability estimation generalization hypothesis testing(HT)

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A theoretical and deep learning hybrid model for predicting surface roughness of diamond-turned polycrystalline materials

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International Journal of Extreme Manufacturing 2023年第3期5卷 620-644页

作者： Chunlei He Jiwang Yan Shuqi Wang Shuo Zhang Guang Chen Chengzu Ren Tianjin Key Laboratory of Equipment Design and Manufacturing Technology Department of Mechanical EngineeringTianjin UniversityTianjin 30054People’s Republic of China Department of Mechanical Engineering Keio UniversityYokohama 223-8522Japan Tianjin Key Laboratory of the Design and Intelligent Control of the Advanced Mechatronical System School of Mechanical EngineeringTianjin University of TechnologyTianjin 300384People’s Republic of China School of Mechanical Engineering Yanshan UniversityQinhuangdao 066004People’s Republic of China

Polycrystalline materials are extensively employed in *** surface roughness significantly affects the working *** defects,particularly grain boundaries,have a great impact on the achieved surface roughness of polycrystalline ***,it is difficult to establish a purely theoretical model for surface roughness with consideration of the grain boundary effect using conventional analytical *** this work,a theoretical and deep learning hybrid model for predicting the surface roughness of diamond-turned polycrystalline materials is *** kinematic–dynamic roughness component in relation to the tool profile duplication effect,work material plastic side flow,relative vibration between the diamond tool and workpiece,etc,is theoretically *** material-defect roughness component is modeled with a cascade forward neural *** the neural network,the ratio of maximum undeformed chip thickness to cutting edge radius RT S,work material properties(misorientation angle θ_(g) and grain size d_(g)),and spindle rotation speed n s are configured as input *** material-defect roughness component is set as the output *** validate the developed model,polycrystalline copper with a gradient distribution of grains prepared by friction stir processing is machined with various processing parameters and different diamond *** with the previously developed model,obvious improvement in the prediction accuracy is observed with this hybrid prediction *** on this model,the influences of different factors on the surface roughness of polycrystalline materials are *** influencing mechanism of the misorientation angle and grain size is quantitatively *** fracture modes,including transcrystalline and intercrystalline fractures at different RTS values,are ***,optimal processing parameters are obtained with a simulated annealing *** experiments are performed with

关键词： diamond turning material-defect roughness component polycrystalline copper neural network simulated annealing algorithm

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Cooperative Local Path Planning for Multi-unmanned Vehicles Formation Using the Enhanced Artificial Potential Field 3rd

Cooperative Local Path Planning for Multi-unmanned Vehicles...

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3rd International Conference on Autonomous Unmanned systems, ICAUS 2023

作者： Zhang, Xin Duan, Xuting Zhou, Jianshan Zhang, Long Opening Project of Key Laboratory of Operation Safety Technology on Transport Vehicles Ministry of Transport PRC Beijing China School of Transportation Science and Engineering Beihang University Beijing China State Key Lab of Intelligent Transportation System Beijing China Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control Beijing China Zhongguancun Laboratory Beijing China National Key Laboratory of Science and Technology on Information System Security Institute of Systems Engineering Beijing China

ISBN: (纸本)9789819711024

An enhanced approach utilizing APF(artificial potential field) method is introduced in this paper. By adopting this approach, the challenge of local minima that may occur when dealing with local path planning for unmanned vehicles is tackled. When the unmanned vehicle approaches an obstacle, a virtual attractive potential field is implemented, utilizing the deviation in the heading angle of the vehicle to control the steering angle. Consequently, the vehicle can avoid obstacles, resolving the issue of local minima and enabling its application in path planning for multi-unmanned vehicles. The ROS with Gazebo simulation platform is used to verify the enhanced algorithm’s effectiveness in multi-unmanned vehicle path planning in an unknown environment. © Beijing HIWING Scientific and Technological Information Institute 2024.

关键词： Motion planning

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Chaos Raman distributed optical fiber sensing

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Light(Science & Applications) 2023年第10期12卷 2105-2121页

作者： Chenyi Wang Jian Li Xinxin Zhou Zijia Cheng Lijun Qiao Xiaohui Xue Mingjiang Zhang College of Physics Taiyuan University of TechnologyTaiyuan 030024 ShanxiChina Key Laboratory of Advanced Transducers and Intelligent Control System of Ministry of Education Taiyuan University of TechnologyTaiyuan 030024 ShanxiChina College of Electrical Information and Optical Engineering Taiyuan University of TechnologyTaiyuan030024ShanxiChina Shanxi-Zheda Institute of Advanced Materials and Chemical Engineering Taiyuan 030032 ShanxiChina

The physics principle of pulse flight positioning is the main theoretical bottleneck that restricts the spatial resolution of the existing Raman distributed optical fiber sensing *** to the pulse width of tens of nanoseconds,the spatial resolution of the existing Raman distributed optical fiber sensing scheme with kilometer-level sensing distance is limited to the meter level,which seriously restricts the development of the optical time-domain reflection *** this paper,a chaos laser is proposed in the context of the physical principle of the Raman scattering effect,and a novel theory of chaos Raman distributed optical fiber sensing scheme is *** scheme reveals the characteristics of chaos Raman scattering light excited by a chaotic signal on the sensing ***,the chaos time-domain compression demodulation mechanism between the temperature variation information and chaos correlation peak is ***,the position of the temperature variation signal is precisely located using the delay time of the chaos correlation peak combined with the chaos pulse flight *** on this novel optical sensing mechanism,an experiment with 10 cm spatial resolution and 1.4 km sensing distance was conducted,and the spatial resolution was found to be independent of the sensing *** the limit of the existing spatial resolution theory,the spatial resolution of the proposed scheme is 50 times higher than that of the traditional *** scheme also provides a new research direction for optical chaos and optical fiber sensing.

关键词： fiber optical scattering

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Energy management of solid oxide fuel cell/lithium battery hybrid system

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CSEE Journal of Power and Energy systems 2022年 1-14页

作者： Li, Dong Zhong, Xiao Bo Xu, Yuan Wu Wei, Chong Yang Zhao, Song Jiang, Jianhua Deng, Zhonghua Fu, Xiaowei Li, Xi School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry Huazhong University of Science and Technology Hubei Wuhan430074 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 Hubei Wuhan430081 China

The research of fuel cell and lithium battery hybrid system has attracted more and more researchers because of its advantages of low emission. However, the lower efficiency of energy management has been a critical factor that obstruct the commercialization of hybrid system. In this work, based on finite element ideas, a solid oxide full cell node model is developed to accurately estimate the operating status of the fuel cell. As for lithium battery, previous energy management studies generally only focused on state of charge, however, state of health is also a key parameter for lithium battery. In this paper, considering the strong coupling between variables we use particle filter algorithm to jointly estimate the state of charge and state of health. To reasonably distribute and manage the energy in the hybrid system, a T-S fuzzy controller for the hybrid system is designed by controlling the state variables in the SOFC model and the state of lithium battery. Finally, our algorithm is verified by DSPACE simulation platform. The result shows that the hybrid system with the energy management strategy not only meets the real-time sharply changing demand but also ensures the hybrid system working efficiently and safely. © 2022 China Electric Power Research Institute. All rights reserved.

关键词： Hybrid systems

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An Adaptive Physics-Informed Neural Network with Two-Stage Learning Strategy to Solve Partial Differential Equations

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Numerical Mathematics(Theory,Methods and Applications) 2023年第2期16卷 298-322页

作者： Shuyan Shi Ding Liu Ruirui Ji Yuchao Han School of Automation and Information Engineering Xi‘an University of TechnologyXi‘an 710048P.R.China National&Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration Xi‘an 710048P.R.China Xi’an University of Technology Shannxi Key Laboratory of Complex System Control and Intelligent Information ProcessingXi‘an 710048P.R.China

Physics-Informed Neural Network(PINN)represents a new approach to solve Partial Differential Equations(PDEs).PINNs aim to solve PDEs by integrating governing equations and the initial/boundary conditions(I/BCs)into a loss ***,the imbalance of the loss function caused by parameter settings usually makes it difficult for PINNs to converge,*** they fall into local *** other words,the presence of balanced PDE loss,initial loss and boundary loss may be critical for the *** addition,existing PINNs are not able to reveal the hidden errors caused by non-convergent boundaries and conduction errors caused by the PDE near the ***,these problems have made PINN-based methods of limited use on practical *** this paper,we propose a novel physics-informed neural network,*** adaptive physics-informed neural network with a two-stage training *** algorithm adds spatio-temporal coefficient and PDE balance parameter to the loss function,and solve PDEs using a two-stage training process:pre-training and formal *** pre-training step ensures the convergence of boundary loss,whereas the formal training process completes the solution of PDE by balancing various loss *** order to verify the performance of our method,we consider the imbalanced heat conduction and Helmholtz equations often appearing in practical *** Klein-Gordon equation,which is widely used to compare performance,reveals that our method is able to reduce the hidden *** results confirm that our algorithm can effectively and accurately solve models with unbalanced loss function,hidden errors and conduction *** codes developed in this manuscript are publicy available at https://***/callmedrcom/ATPINN.

关键词： Physics informed neural networks partial differential equations two-stage learning scientific computing

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Cooperative Prediction Approach for the Small-Signal Stability Margin of Renewable Power system Based on Vertical Federated Learning 3

Cooperative Prediction Approach for the Small-Signal Stabili...

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3rd International Conference on Power systems and Electrical technology, PSET 2024

作者： Lai, Xiang Chen, Zhihao Liu, Yun Dai, Tianchen Cai, Haiqing Gu, Haohan Chen, Wei South China University of Technology School of Electric Power Engineering Guangzhou China CSG Electric Power Research Institute State Key Laboratory of HVDC Guangzhou China Laboratory of Intelligent Operation and Control for New Energy Power System Guangdong Provincial Key Guangzhou China

ISBN: (纸本)9798350353204

Since sub-synchronous oscillations have been frequently witnessed in large-scale wind farms under weak grid conditions, it is significant to predict the critical stability situations in short term so that timely preventative actions can be taken to avoid potential small-signal stability (SS) risks. To this end, this paper proposes a SS prediction approach based on the grid strength metric of generalized short-circuit ratio (gSCR) under the mechanism of vertical federated learning. Specifically, the improved ridge regression prediction model is employed and its corresponding distributed training and the distributed predicting are derived by utilizing the separable characteristic of gradient vector calculation in the context of federated learning. On this basis, the original largescale predicting problem is decomposed into a large number of small-scale subproblems, and each subproblem is calculated locally by the corresponding wind farm. The feature data information of adjacent wind farms can be effectively used to improve the accuracy of the prediction of the stability margin while ensuring the data privacy and security of all participants. Finally, simulation results illustrate the effectiveness and efficiency of the proposed approach under various scenarios. © 2024 IEEE.

关键词： Federated learning

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