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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Li, Taoman Zhu, Fei Gan, Zihan Zhou, Zexing Wang, Xiaotao Lu, Renzhi China Ship Scientific Research Center Wuxi214082 China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control Wuhan430074 China Wuhan University of Science and Technology Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan430081 China

ISBN: (纸本)9798350331820

This article proposes a multi-agent deep reinforce-ment learning algorithm to control a fleet of unmanned surface vessels (USVs) that encircle and capture sea targets. First, a simulation environment for USVs is established based on a dynamic model;two-dimensional control variables are used to control movements in three directions. Second, the multi-agent deep deterministic policy gradient (MADDPG) algorithm is employed to achieve intelligent control of the USVs, using a reward function based on certain prior knowledge. Finally, centralized training and decentralized execution are used to complete the offline learning of multiple agents, and continuous action decisions are made based on the observations of the USV sensors. Simulations demonstrate that the method can capture stationary moving sea targets with any number of multi-agents under disturbances, and exhibits strong robustness and practicality. © 2023 IEEE.

关键词： Reinforcement learning

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A Fuzzy Creative Works Generation Algorithm Based on Graph Neural Network 6th

A Fuzzy Creative Works Generation Algorithm Based on Graph N...

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6th Euro-China Conference on intelligent Data Analysis and Applications, ECC 2019

作者： Zhang, Fuquan Wang, Yiou Zhao, Guifen Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China Beijing Institute of Science and Technology Information Beijing100044 China

ISBN: (纸本)9789811650352

A fuzzy creative works generation algorithm based on graph neural network is proposed. Firstly, the multi-label fuzzy creative data set is constructed. Secondly, fuzzy logical correlations between creative objects are dynamically calculated by graphical neural networks to capture relevant digital objects and their relationships. Thirdly, the projectiles, boundary markers and location words of the creative scene objects are generated by analyzing related attributes of each entity based on graph neural network. Fuzzy creative works are automatically generated by adjusting the appropriate location. Finally, the experiments are deployed, and experimental results show that the accuracy rate of the proposed algorithm is improved by 9.6% on average compared with the manual selection, which indicates the feasibility of the proposed algorithm replacing the manual selection. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fuzzy neural networks

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Iterative-Reweighting-Based Robust Iterative-Closest-Point Method

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Journal of Shanghai Jiaotong university(Science) 2021年第5期26卷 739-746页

作者： ZHANG Jianlin ZHOU Xuejun YANG Ming Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai 200240China Noblelift Intelligent Equipment Co. Ltd.Huzhou 313100ZhejiangChina

In point cloud registration applications,noise and poor initial conditions lead to many false *** matches significantly degrade registration accuracy and speed.A penalty function is adopted in many robust point-to-point registration methods to suppress the influence of false ***,after applying a penalty function,problems cannot be solved in their analytical forms based on the introduction of ***,most existing methods adopt the descending *** this paper,a novel iterative-reweighting-based method is proposed to overcome the limitations of existing *** proposed method iteratively solves the eigenvectors of a four-dimensional matrix,whereas the calculation of the descending method relies on solving an eight-dimensional ***,the proposed method can achieve increased computational *** proposed method was validated on simulated noise corruption data,and the results reveal that it obtains higher efficiency and precision than existing methods,particularly under very noisy *** results for the KITTI dataset demonstrate that the proposed method can be used in real-time localization processes with high accuracy and good efficiency.

关键词： point cloud registration iterative reweighting iterative closest-point(ICP) robust localization

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Bipartite flock control of multi-agent systems

Bipartite flock control of multi-agent systems

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第三十二届中国控制会议

作者： Ming-Can Fan Hai-Tao Zhang Key Laboratory of Image Processing and Intelligent Control Department of Control Science and Engineering State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology

ISBN: (纸本)9781479900305

This paper addresses the bipartite flock control problem with or without a virtual leader. Using structurally balanced signed graph theory and LaSalle’s invariance principle, we prove that the algorithm guarantees a bipartite flocking behavior. In such a collective motion, the whole group separates into two clusters, in each of which all individuals move with the same direction. Meanwhile, every pair of agents in different clusters moves with opposite directions. Moreover, all agents in the two separated clusters approach a common velocity magnitude, and collision avoidance among each cluster is ensured as well. Finally, the proposed bipartite flock control method are examined by numerical simulations. The bipartite flocking motion addressed by this paper has its references in both natural collective motions and human group behaviors such as predator-prey and panic escaping scenarios.

关键词： Flocking Multi-agent systems Signed graph Virtual leader

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Upper limb rehabilitation robot integrated with motion intention recognition and virtual reality environment

Upper limb rehabilitation robot integrated with motion inten...

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Chinese control Conference (CCC)

作者： Jun Wu Jian Huan Yongji Wang Key Laboratory of Image Processing and Intelligent Control Department of Control Science & Engineering Huazhong University of Science and Technology Wuhan 430074 China

Robotic-assisted therapy is of benefit to the recovery of upper limb motor function for the patients survived stoke. Whereas, there are few emphases on the patients' motion intention during the rehabilitation process. The goal of this study is to combine the control strategies based on patients' motion intention with an upper limb rehabilitation robot to improve the recovery for the patients. In this paper, we propose an integrated robot-assist rehabilitation system, in which a 3 degree-of-freedom (DOF) exoskeletal rehabilitation robot, an EMG-based intention recognition module and a VR game environment are seamlessly combined. According toharacteristics of EMG signals, the wavelet package analysis approach is applied to extract the features of EMG. The node energy is used to construct the feature vector instead of the original coefficients of wavelet package decomposition to resolve the time-invariance problem. Then feature projection results in the singularity problem of with-in scatter matrix during the feature dimension reduction. To overcome the disadvantage of the with-in scatter matrix, this paper uses a recursive algorithm which is proposed in our previous work. The reduced feature vector is recognized by a neural network classifier and the output of the classifier is used for the control inputs. Preliminary experiments are also performed to implement the control of the rehabilitation robotic system by using the proposed EMG reorganization method, together with a dart game realized in the virtual reality environment. Experimental results show that the performance of motion intention recognition is satisfactory and the entire integrated system is feasible.

关键词： Robots Feature extraction Electromyography Classification algorithms Wavelet packets Games

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A reentry corridor calculation method for reusable Launch Vehicles based on skipping trajectory

A reentry corridor calculation method for reusable Launch Ve...

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Chinese control Conference (CCC)

作者： Kunliang Wei Lei Liu Yongji Wang Songtao Chang Department of Control Science and Engineering Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control Wuhan 430074 China

A new method to calculate the corridor of Reentry Launch Vehicles is proposed, based on their dynamic properties. The upper boundary of the corridor can be calculated by the skipping trajectories from the lower boundary of the corridor. By combination with a specific kinematic equations and a numerical simulation, the reliability and optimality of the method is analyzed. The results also show that the traditional reentry corridor estimated by quasi-equilibrium gliding conditions has its conservativeness, the reentry corridor that the new method calculated can better reflect the RLV's maneuvers abilities. Moreover, compared to the general methods, the direct and indirect methods to calculate the corridor, which are usually used to solve optimization problems, the proposed method has a smaller amount of calculation, shorter calculation time, stronger convergence, thus has stronger practicability.

关键词： Electronic mail Vehicles Trajectory Radio frequency Educational institutions Laboratories image processing

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A weakly universal spiking neural P system

A weakly universal spiking neural P system

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International Conference on Bio-Inspired Computing: Theories and Applications, BICTA

作者： Xiangxiang Zeng Chun Lu Linqiang Pan Key Laboratory of Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan Hubei China

Looking for small universal computing devices is a natural and well investigated topic in computer science. Recently, this topic was also investigated in the framework of spiking neural P systems. One of small universality results is that a small weakly universal extended spiking neural P system with 12 neurons was constructed. In this paper, a new way is introduced for simulating register machines by spiking neural P systems, where only one neuron is used for all instructions of register machine; in this way, we can use less neurons to construct universal spiking neural P system. Specifically, we give a smaller weakly universal spiking neural P system that uses extended rules and has only 9 neurons.

关键词： Neurons Tin Registers Biomembranes Computational modeling Encoding Decoding intelligent control control systems Computer science

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Distributed Neighbor Selection on Matrix-weighted Networks 38

Distributed Neighbor Selection on Matrix-weighted Networks

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38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023

作者： Wei, Haoyu Pan, Lulu Shao, Haibin Li, Dewei Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai200240 China

ISBN: (纸本)9798350303636

This paper explores distributed neighbor selection in matrix-weighted networks, where a matrix-valued weight captures the interdependencies among agents' states. We demonstrate the feasibility of preserving and, in certain cases, improving the convergence rate of matrix-weighted networks by having agents exclusively engage with a subset of their neighbors. Leveraging the inherent monotonicity of Laplacian matrix eigenvectors, we introduce a novel neighbor selection criterion that reduces network connections while enhancing convergence rates. Existing neighbor selection strategies often require acquiring information from all nodes before making decisions. In order to propose a distributed neighbor selection strategy for matrix-weighted networks, we establish a numerical relationship between Laplacian eigenvectors and a locally measurable metric. In essence, this paper extends prior findings from scalar-weighted networks to matrix-weighted networks. © 2023 IEEE.

关键词： convergence rate Distributed neighbor selection Laplacian matrix matrix-weighted networks

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ICP algorithm point cloud registration method based on a new improved Harris Hawk algorithm

ICP algorithm point cloud registration method based on a new...

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2021 China Automation Congress, CAC 2021

作者： Wang, Chen Jiao, Shangbin Gao, Rui Li, Yuxing School of Automation and Information Engineering Xi'an University of Technology Xi'an China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China School of Electronic and Electrical Engineering Baoji University of Arts and Sciences Baoji China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Baoji China

ISBN: (纸本)9781665426473

To address shortcomings of ICP algorithm, which is sensitive to the initial position and easy to fall into local optimum, we propose a Harris Hawks Optimizer (HHO) point cloud registration method combining PCA preprocessing and parallel population initialization. The PCA method is used to align the point clouds with the main axes and perform the main axes correction. Since the six input variables in the population initialization stage of HHO point cloud registration are different and independent, the initial population of HHO algorithm is initialized by the parallel initialization method. The results show that the improved algorithm is able to successfully overwhelm the fault of ICP algorithm, and its global seek performance and optimization accuracy are more advantageous, and the algorithm operates efficiently, with high registration accuracy and robustness. © 2021 IEEE

关键词： PCA HHO Parallel Computing ICP

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Synchronization of Switched Neural Networks with Communication Delays via the Event-Triggered control

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IEEE Transactions on Neural Networks and Learning Systems 2017年第10期28卷 2334-2343页

作者： Wen, Shiping Zeng, Zhigang Chen, Michael Z. Q. Huang, Tingwen Guangdong Province Key Lab of Digital Manufacturing Equipment Key Laboratory of Image Processing Intelligent Control of Education Ministry of China School of Automation Guangdong HUST Industrial Technology Research Institute Huazhong University of Science Technology Wuhan Hubei430074 China Department of Mechanical Engineering University of Hong Kong Hong Kong Hong Kong Texas A M University at Qatar DohaTX5825 United States

This paper addresses the issue of synchronization of switched delayed neural networks with communication delays via event-triggered control. For synchronizing coupled switched neural networks, we propose a novel event-triggered control law which could greatly reduce the number of control updates for synchronization tasks of coupled switched neural networks involving embedded microprocessors with limited on-board resources. The control signals are driven by properly defined events, which depend on the measurement errors and current-sampled states. By using a delay system method, a novel model of synchronization error system with delays is proposed with the communication delays and event-triggered control in the unified framework for coupled switched neural networks. The criteria are derived for the event-triggered synchronization analysis and control synthesis of switched neural networks via the Lyapunov-Krasovskii functional method and free weighting matrix approach. A numerical example is elaborated on to illustrate the effectiveness of the derived results. © 2012 IEEE.

关键词： Synchronization

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