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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

Composite-mask GAN based on refined optical flow and disparity map for SLAM visual odometry

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Optoelectronics Letters 2025年

作者： JI Yuehui JIANG Jingwei LIU Junjie SONG Yu GAO Qiang School of Electrical Engineering and automation， Tianjin University of Technology Tianjin key laboratory of control Theory and Application for complex systems

Although deep learning methods have been widely applied in slam visual odometry over the past decade with impressive improvements， the accuracy remains limited in complex dynamic environments. In this paper， a composite mask-based generative adversarial network is introduced to predict camera motion and binocular depth maps. Specifically， a perceptual generator is constructed to obtain the corresponding parallax map and optical flow from between two neighboring frames. Then， an iterative pose improvement strategy is proposed to improve the accuracy of pose estimation. Finally， a composite mask is embedded in the discriminator to sense structural deformation in the synthesized virtual image， thereby increasing the overall structural constraints of the network model， improving the accuracy of camera pose estimation， and reducing drift issues in the Visual Odometer. Detailed quantitative and qualitative evaluations on the KITTI dataset show that the proposed framework outperforms existing conventional， supervised learning and unsupervised depth VO methods， providing better results in both pose estimation and depth estimation.

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A Facial Expression Emotion Recognition Based Human-robot Interaction System

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IEEE/CAA Journal of Automatica Sinica 2017年第4期4卷 668-676页

作者： Zhentao Liu Min Wu Weihua Cao Luefeng Chen Jianping Xu Ri Zhang Mengtian Zhou Junwei Mao School of automation China University of Geosciences hubei key laboratory of advanced control and intelligent automation for complex systems IEEE

A facial expression emotion recognition based human-robot interaction(FEER-HRI) system is proposed, for which a four-layer system framework is designed. The FEERHRI system enables the robots not only to recognize human emotions, but also to generate facial expression for adapting to human emotions. A facial emotion recognition method based on2D-Gabor, uniform local binary pattern(LBP) operator, and multiclass extreme learning machine(ELM) classifier is presented,which is applied to real-time facial expression recognition for robots. Facial expressions of robots are represented by simple cartoon symbols and displayed by a LED screen equipped in the robots, which can be easily understood by human. Four scenarios,i.e., guiding, entertainment, home service and scene simulation are performed in the human-robot interaction experiment, in which smooth communication is realized by facial expression recognition of humans and facial expression generation of robots within 2 seconds. As a few prospective applications, the FEERHRI system can be applied in home service, smart home, safe driving, and so on.

关键词： Emotion generation facial expression emotion recognition(FEER) human-robot interaction(HRI) system design

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Modeling for coke quality prediction using Gaussian function and SGA

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Science China(Information Sciences) 2022年第1期65卷 282-284页

作者： Yan YUAN Qilin QU Weihua CAO Min WU School of automation China University of Geosciences hubei key laboratory of advanced control and intelligent automation for complex systems

Dear editor,Recently, we made some new attempts at predicting coke quality. Coke quality prediction provides important guidance for coal blending, improving the quality of coke, and reducing the cost. Prediction usually involves selection of the coal blending parameters and selection of the modeling methods. In recent years, traditional coal quality indicators such as coal impurity(ash Ad, sulfur St.d),

关键词： Modeling for coke quality prediction using Gaussian function and SGA MSE Gaussian

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Design and Hardware-in-the-loop Validation:A Fractional Full Feed-forward Method for Grid Voltage in LCL Grid-connected Inverter systems

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CSEE Journal of Power and Energy systems 2023年第5期9卷 1720-1731页

作者： Qingyi Wang Binlei Ju Yudi Lei Dan Zhou Shuai Yin Danyun Li the School of automation China University of Geosciencesand Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex SystemsWuhan 430074China.

The harmonic disturbance in the background grid is a problem that must be considered in the design of a gridconnected ***,the full feed-forward method cannot completely suppress the harmonic disturbance in theory and is sensitive to *** tackle these problems,a fractional full feed-forward method of grid voltage is proposed in this ***,the mathematical model of the full feed-forward method is deduced,and the differences with the theoretical solution,which can suppress all harmonics,are ***,the parameter equation,the harmonic suppression performance,stability analysis and the implementation process of this method are *** with the full feed-forward method,the proposed method not only further improves the harmonic suppression performance,but also reduces the order of the mathematical model of the differential term in the feed-forward *** addition,the proposed method can be used to flexibly design feed-forward coefficients by selecting the order of suppressed ***,the proposed method is validated by a hardware-in-the-loop experiment on a MT real-time control platform NI PXIE-1071.

关键词： Fractional differential full feed-forward method hardware-in-the-loop LCL inverter

Estimation and compensation of periodic disturbance using internal-model-based equivalent-input-disturbance approach

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Science China(Information Sciences) 2022年第8期65卷 150-163页

作者： Qicheng MEI Jinhua SHE Zhentao LIU Min WU School of automation China University of Geosciences hubei key laboratory of advanced control and intelligent automation for complex systems School of Engineering Tokyo University of Technology

This paper presents an improved equivalent-input-disturbance(EID) approach to deal with periodic disturbances. The approach has two degrees of freedom. One is an improved EID compensator, in which a repetitive controller is inserted in this study. The other is a conventional servo system for a reference *** improved EID compensator estimates and compensates for periodic disturbances without steady-state error, and the servo system ensures a satisfactory tracking performance. The improved EID compensator is designed using the linear-matrix-inequality(LMI) method. Three parameters in an LMI are selected using the particle-swarm-optimization(PSO) algorithm. The state-feedback gain of the conventional servo system is designed using the linear-quadratic-regulator(LQR) method. Simulation results of a rotational control system demonstrate the validity of the approach and its advantage over others.

关键词： equivalent input disturbance (EID) linear matrix inequality (LMI) linear quadratic regulator (LQR) particle swarm optimization (PSO) periodic disturbance repetitive control

Group consensus of multi-agent systems with additive noises

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Science China(Information Sciences) 2022年第10期65卷 242-255页

作者： Chuanjian LI Xiaofeng ZONG School of automation China University of Geosciences hubei key laboratory of advanced control and intelligent automation for complex systems Engineering Research Center of intelligent Technology for Geo-Exploration Ministry of Education

This paper is concerned with group consensus of multi-agent systems(MASs) that consist of two groups in additive noise environments. First, a control protocol is proposed based on the state information of each agent’s neighbors corrupted by additive noises. Second, some sufficient conditions and necessary conditions are obtained for the following two types of group consensus behaviors.(1) Pure group consensus:agents in both groups have the same behavior(weak consensus or strong consensus);(2) hybrid group consensus: agents in different groups achieve different consensus behaviors. It is revealed that the influence between the two groups should be attenuated such that the MASs can achieve group consensus in additive noise environments, i.e., the affected group must fight against the influence that comes from another ***, some simulation examples are given to illustrate the feasibility of the theoretical results.

关键词： group consensus multi-agent systems additive noises pure group consensus hybrid group consensus

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Fixed-time stabilization of discontinuous spatiotemporal neural networks with time-varying coefficients via aperiodically switching control

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Science China(Information Sciences) 2023年第5期66卷 182-195页

作者： Xiaofang HU Leimin WANG Chuan-Ke ZHANG Xiongbo WAN Yong HE School of automation China University of Geosciences hubei key laboratory of advanced control and intelligent automation for complex systems Engineering Research Center of intelligent Technology for Geo-Exploration Ministry of Education

This paper focuses on the challenge of fixed-time control for spatiotemporal neural networks(SNNs) with discontinuous activations and time-varying coefficients. A novel fixed-time convergence lemma is proposed, which facilitates the handling of time-varying coefficients of SNNs and relaxes the restriction on the non-positive definiteness of the derivative of the Lyapunov function. Besides, a more flexible and economical aperiodically switching control technique is presented to stabilize SNNs within a fixed time,efectively reducing the amount of information transmission and control costs. Under the newly established fixed-time convergence lemma and aperiodically switching controller, many more general algebraic conditions are deduced to ensure the fixed-time stabilization of SNNs. Numerical examples are provided to manifest the validity of the results.

关键词： spatiotemporal neural networks discontinuous activations time-varying coefficients fixed-time stabilization aperiodically switching control

Simplified outlier detection for improving the robustness of a fuzzy model

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Science China(Information Sciences) 2020年第4期63卷 218-220页

作者： Yali JIN Weihua CAO Min WU Yan YUAN School of automation China University of Geosciences hubei key laboratory of advanced control and intelligent automation for complex systems China University of Geosciences

Dear editor,Fuzzy models have been widely employed in various fields, such as complex industrial process modeling, classification, function approximation, and time series prediction. Extracting fuzzy rules is very important as they determine the capabilities of the fuzzy model. In recent years, data-based methods such as fuzzy clustering, genetic algorithm, and neural networks [1–3], have been pro-

关键词： Simplified outlier detection for improving the robustness of a fuzzy model Gaussian