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Targets capture by distributed active swarms via bio-inspired reinforcement learning

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Science China(Physics,Mechanics & Astronomy) 2025年第1期Mechanics & Astronomy) .卷 210-221页

作者： Kun Xu Yue Li Jun Sun Shuyuan Du Xinpeng Di Yuguang Yang Bo Li institute of Biomechanics and Medical Engineering Applied Mechanics Laboratory Department of Engineering MechanicsTsinghua University institute of Nuclear and New Energy Technology Tsinghua University Shanghai Aerospace Control Technology institute Shanghai Key Laboratory of Aerospace Intelligent Control Technology College of automation Engineering Nanjing University of Aeronautics and Astronautics

Natural swarms arranged from cells to herds are usually decentralized but display intriguing collective intelligence in coordinating individuals across large scales to efficiently achieve their common goals. Learning from nature may provide new strategies for controlling collective dynamics of synthetic swarms to accomplish specific functions. Here, we present a bioinspired computational framework that steers distributed active swarms to collectively capture and merge targets via reinforcement learning. We exploit collective milling structures of natural herds to cage the targets, and adopt a switching control policy inspired by sperms' chiral dynamics to optimize the trajectories of individuals, through which the active swarms can selforganize to enclose single or multiple distant targets in a dynamical, adaptive and scalable manner. There exists a critical swarm size, beyond which the excessive competition between agents would generate large mechanical forces, leading to capture instability but enabling the transition from short-distance to long-distance merging capture of multiple targets. This work provides physical insights into distributed active swarms and could offer a multilevel, decentralized strategy toward controlling swarm robotics in wide applications such as bio-medical devices, machine immunity, and target clearance.

关键词： swarm intelligence robotics multi-tasks artificial intelligence bionics

Nanograting‑Based Dynamic Structural Colors Using Heterogeneous Materials

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Nano-Micro Letters 2025年第3期17卷 138-151页

作者： Jingang Wang Haibo Yu Jianchen Zheng Yuzhao Zhang Hongji Guo Ye Qiu Xiaoduo Wang Yongliang Yang Lianqing Liu State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016People’s Republic of China University of Chinese Academy of Sciences Beijing 100049People’s Republic of China

Dynamic structuralcolors can change in response todifferent environmental *** ability remains effectiveeven when the size of the speciesresponsible for the structural coloris reduced to a few micrometers,providing a promising sensingmechanism for solving microenvironmentalsensing problems inmicro-robotics and ***, the lack of dynamicstructural colors that can encoderapidly, easily integrate, and accuratelyreflect changes in physical quantities hinders their use in microscale sensing applications. Herein, we present a 2.5-dimensionaldynamic structural color based on nanogratings of heterogeneous materials, which were obtained by interweaving a pH-responsive hydrogelwith an IP-L photoresist. Transverse gratings printed with pH-responsive hydrogels elongated the period of longitudinal grating in the swollenstate, resulting in pH-tuned structural colors at a 45° incidence. Moreover, the patterned encoding and array printing of dynamic structuralcolors were achieved using grayscale stripe images to accurately encode the periods and heights of the nanogrid structures. Overall, dynamicstructural color networks exhibit promising potential for applications in information encryption and in situ sensing for microfluidic chips.

关键词： Dynamic structural colors Four-dimensional printing pH-responsive Nanogrid Heterogeneous materials

Multi-model ensemble learning for battery state-of-health estimation:Recent advances and perspectives

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Journal of Energy Chemistry 2025年第1期100卷 739-759页

作者： Chuanping Lin Jun Xu Delong Jiang Jiayang Hou Ying Liang Zhongyue Zou Xuesong Mei State Key Laboratory for Manufacturing Systems Engineering Xi'an Jiaotong University Shaanxi Key Laboratory of Intelligent Robots School of Mechanical Engineering Xi'an Jiaotong University Department of Electrical Engineering and automation Luoyang Institute of Science and Technology School of Mechanical Engineering Hangzhou Dianzi University

The burgeoning market for lithium-ion batteries has stimulated a growing need for more reliable battery performance monitoring. Accurate state-of-health(SOH) estimation is critical for ensuring battery operational performance. Despite numerous data-driven methods reported in existing research for battery SOH estimation, these methods often exhibit inconsistent performance across different application scenarios. To address this issue and overcome the performance limitations of individual data-driven models,integrating multiple models for SOH estimation has received considerable attention. Ensemble learning(EL) typically leverages the strengths of multiple base models to achieve more robust and accurate outputs. However, the lack of a clear review of current research hinders the further development of ensemble methods in SOH estimation. Therefore, this paper comprehensively reviews multi-model ensemble learning methods for battery SOH estimation. First, existing ensemble methods are systematically categorized into 6 classes based on their combination strategies. Different realizations and underlying connections are meticulously analyzed for each category of EL methods, highlighting distinctions, innovations, and typical applications. Subsequently, these ensemble methods are comprehensively compared in terms of base models, combination strategies, and publication trends. Evaluations across 6 dimensions underscore the outstanding performance of stacking-based ensemble methods. Following this, these ensemble methods are further inspected from the perspectives of weighted ensemble and diversity, aiming to inspire potential approaches for enhancing ensemble performance. Moreover, addressing challenges such as base model selection, measuring model robustness and uncertainty, and interpretability of ensemble models in practical applications is emphasized. Finally, future research prospects are outlined, specifically noting that deep learning ensemble is poised to advance ens

关键词： Contrastive Learning

Joint Probabilistic Scheduling and Resource Allocation for Wireless Networked Control Systems

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IEEE/CAA Journal of Automatica Sinica 2025年第1期 258-260页

作者： Meng Zheng Lei Zhang Wei Liang IEEE the State Key Laboratory of Robotics and the Key Laboratory of Networked Control Systems Shenyang Institute of Automation Chinese Academy of Sciences the University of Chinese Academy of Sciences

Dear Editor,This letter presents a joint probabilistic scheduling and resource allocation method(PSRA) for 5G-based wireless networked control systems(WNCSs). As a control-aware optimization method, PSRA minimizes the linear quadratic Gaussian(LQG) control cost of WNCSs by optimizing the activation probability of subsystems, the number of uplink repetitions, and the durations of uplink and downlink phases. Simulation results show that PSRA achieves smaller LQG control costs than existing works.

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Output Consensus of Heterogeneous Linear MASs via Adaptive Event-Triggered Feedback Combination Control

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IEEE/CAA Journal of Automatica Sinica 2025年第1期 285-287页

作者： Shuo Yuan Chengpu Yu Jian Sun the School of automation Beijing Institute of Technology the Beijing institute of Technology Chongqing Innovation Center

Dear Editor,This letter studies output consensus problem of heterogeneous linear multiagent systems over directed graphs. A novel adaptive dynamic event-triggered controller is presented based only on the feedback combination of the agent's own state and neighbors' output,which can achieve exponential output consensus through intermittent communication. The controller is obtained by solving two linear matrix equations, and Zeno behavior is excluded.

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NBCM with Flat Roll in the Finish Stand

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Journal of Materials Science & Technology 2004年第2期20卷 233-235页

作者： XiangweiKONG HezhouYE JianzhongXUt GuodongWANG XianghuaLIU gunweiZHANG StateKeyLaboratoryofRolling&automation NEUShengyang110004China instituteofMetalResearch ChineseAcademyofSciencesShenyang110016China

With the development of the market, it becomes a demanding task for producers to make flexible production schedules to shorten production cycle. Schedule-free rolling is needed. If the CVC work roll of the F6 and F7 stands in certain 2050 mm hot rolled strip mill are substituted by flat roll (aim to SFR) and the strip profile is controlled by the existing bending force, the control ability is not adequate. This fact has been tested through on-line experiment and has been given in this article. The NBCM (new backup roll crowning method) is recommended to improve the profile control ability. Finally the plastic deformation of the strip and the elastic deformation of the roll are analyzed by employing coupled calculation of rigid-plastic finite element method and G-function method, and the optimal range of the crown of backup roll is given theoretically.

关键词： SFR NBCM Conventional roll Rigid-Plastic FEM G-function method Coupled calculation

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Steel surface defect detection based on lightweight YOLOv7

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Optoelectronics Letters 2025年

作者： Shi Tao Wu Rongxin Zhu Wenxu Ma Qingliang School of Electrical Engineering and automation， Tianjin University of Technology North China University of Science and Technology， College of Electrical Engineering Guangzhou institute of Measurement and Testing Technology

Aiming at the problems of low detection efficiency and difficult positioning of traditional steel surface defect detection methods， a lightweight steel surface defect detection model based on YOLOv7 is proposed. First， a CSS Block module is proposed， which uses more lightweight operations to obtain redundant information in the feature map， reduces the amount of computation， and effectively improves the detection speed. Secondly， the improved SPPCSPC structure is adopted to ensure that the model can also pay attention to the defect location information while predicting the defect category information， obtain richer defect features. In addition， the convolution operation in the original model is simplified， which significantly reduces the size of the model and helps to improve the detection speed. Finally， using EIOU Loss to focus on high-quality Anchors， speed up convergence and improve positioning accuracy. Experiments were carried out on the NEU-DET steel surface defect dataset. Compared with the original YOLOv7 model， the number of parameters of this model was reduced by 40%， the FPS reached 112， and the average accuracy reached 79.1%.， the detection accuracy and speed have been improved， which can meet the needs of steel surface defect detection.

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Industrial Control Anomaly Detection Based on Distributed Linear Deep Learning

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Computers, Materials & Continua 2025年第1期82卷 1129-1150页

作者： Shijie Tang Yong Ding Huiyong Wang School of Computer Science and Information Security Guilin University of Electronic TechnologyGuilin541004China School of Electronic Engineering and automation Guilin University of Electronic TechnologyGuilin541004China Guangxi Engineering Research Center of Industrial Internet Security and Blockchain Guilin University of Electronic TechnologyGuilin541004China institute of Cyberspace Technology HKCT Institute for Higher EducationHong Kong999077China School of Mathematics&Computing Science Guilin University of Electronic TechnologyGuilin541004China

As more and more devices in Cyber-Physical Systems(CPS)are connected to the Internet,physical components such as programmable logic controller(PLC),sensors,and actuators are facing greater risks of network attacks,and fast and accurate attack detection techniques are *** key problem in distinguishing between normal and abnormal sequences is to model sequential changes in a large and diverse field of time *** address this issue,we propose an anomaly detection method based on distributed deep *** method uses a bilateral filtering algorithm for sequential sequences to remove noise in the time series,which can maintain the edge of discrete *** use a distributed linear deep learning model to establish a sequential prediction model and adjust the threshold for anomaly detection based on the prediction error of the validation *** method can not only detect abnormal attacks but also locate the sensors that cause *** conducted experiments on the Secure Water Treatment(SWAT)and Water Distribution(WADI)public *** experimental results show that our method is superior to the baseline method in identifying the types of attacks and detecting efficiency.

关键词： Anomaly detection CPS deep learning MLP(multi-layer perceptron)

Three-dimensional path planning for unmanned aerial vehicle based on interfered fluid dynamical system

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Chinese Journal of Aeronautics 2015年第1期28卷 229-239页

作者： Wang Honglun Lyu Wentao Yao Peng Liang Xiao Liu Chang Science and Technology on Aircraft Control Laboratory Beihang University School of automation Science and Electrical Engineering Beihang University The 28th Research institute of China Electronics Technology Group Corporation School of automation Shengyang Aerospace University

This paper proposes a method for planning the three-dimensional path for low-flying unmanned aerial vehicle（UAV） in complex terrain based on interfered fluid dynamical system（IFDS） and the theory of obstacle avoidance by the flowing stream. With no requirement of solutions to fluid equations under complex boundary conditions, the proposed method is suitable for situations with complex terrain and different shapes of obstacles. Firstly, by transforming the mountains, radar and anti-aircraft fire in complex terrain into cylindrical, conical, spherical, parallelepiped obstacles and their combinations, the 3D low-flying path planning problem is turned into solving streamlines for obstacle avoidance by fluid flow. Secondly, on the basis of a unified mathematical expression of typical obstacle shapes including sphere, cylinder, cone and parallelepiped, the modulation matrix for interfered fluid dynamical system is constructed and 3D streamlines around a single obstacle are obtained. Solutions to streamlines with multiple obstacles are then derived using weighted average of the velocity field. Thirdly, extra control force method and virtual obstacle method are proposed to deal with the stagnation point and the case of obstacles＇ overlapping respectively. Finally, taking path length and flight height as sub-goals, genetic algorithm（GA） is used to obtain optimal 3D path under the maneuverability constraints of the UAV. Simulation results show that the environmental modeling is simple and the path is smooth and suitable for UAV. Theoretical proof is also presented to show that the proposed method has no effect on the characteristics of fluid avoiding obstacles.

关键词： obstacles terrain dynamical avoidance unmanned constraints overlapping unified flying spherical

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