IEEE Transactions on Systems Man and Cybernetics Part A-Systems and Humans最新文献_第9页

Stability Analysis of Systems With Time-Varying Delay via Improved Lyapunov–Krasovskii Functionals 基于改进Lyapunov-Krasovskii泛函的时变时滞系统稳定性分析

IEEE Transactions on Systems Man and Cybernetics Part A-Systems and Humans Pub Date : 2021-04-01 DOI: 10.1109/TSMC.2019.2914367

Fei Long, Chuan‐Ke Zhang, Lin Jiang, Yong He, Min Wu

引用次数: 53

Event-Triggered Distributed H∞ Constrained Control of Physically Interconnected Large-Scale Partially Unknown Strict-Feedback Systems 物理互连的大规模部分未知严格反馈系统的事件触发分布式H∞约束控制

IEEE Transactions on Systems Man and Cybernetics Part A-Systems and Humans Pub Date : 2021-04-01 DOI: 10.1109/TSMC.2019.2914160

Luy Tan Nguyen

{"title":"Event-Triggered Distributed H∞ Constrained Control of Physically Interconnected Large-Scale Partially Unknown Strict-Feedback Systems","authors":"Luy Tan Nguyen","doi":"10.1109/TSMC.2019.2914160","DOIUrl":"https://doi.org/10.1109/TSMC.2019.2914160","url":null,"abstract":"In this paper, an event-triggered distributed <inline-formula> <tex-math notation=\"LaTeX\">${ {mathcal {H}}_{infty }}$ </tex-math></inline-formula> constrained control algorithm is designed for physically interconnected large-scale partially unknown strict-feedback systems with constrained-input and external disturbance. The advantage of both physical interconnection and communication is synchronously exploited for the scheme. First, an event-triggered feedforward control policy is proposed to transform control of physically interconnected large-scale systems into equivalent event-triggered control of decoupled multiagent systems. Then, an event-triggering condition and an event-triggered feedback control algorithm are designed to learn the optimal control policy and the disturbance policy in the worst case. The algorithm eliminates identifier, actor, and disturber neural networks and also relaxes the persistent excitation condition. It guarantees that the closed-loop dynamics is stabilized and the cost function is converged to the bounded <inline-formula> <tex-math notation=\"LaTeX\">${mathcal {L}}_{2}$ </tex-math></inline-formula>-gain optimal value while the Zeno phenomenon is excluded. Finally, the effectiveness of the proposed algorithm is verified through simulation results of event-triggered distributed control of a physically interconnected constrained-torques multimobile robot system.","PeriodicalId":55007,"journal":{"name":"IEEE Transactions on Systems Man and Cybernetics Part A-Systems and Humans","volume":"22 1","pages":"2444-2456"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83552793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 25

Global Optimization: A Distributed Compensation Algorithm and its Convergence Analysis 全局优化:一种分布式补偿算法及其收敛性分析

IEEE Transactions on Systems Man and Cybernetics Part A-Systems and Humans Pub Date : 2021-04-01 DOI: 10.1109/TSMC.2019.2912825

Wen-Ting Lin, Yan-wu Wang, Chaojie Li, Jiang‐Wen Xiao

引用次数: 4

Multiobjective Multiple Neighborhood Search Algorithms for Multiobjective Fleet Size and Mix Location-Routing Problem With Time Windows 多目标多邻域搜索算法求解多目标机队规模和带时间窗的混合位置-路由问题

IEEE Transactions on Systems Man and Cybernetics Part A-Systems and Humans Pub Date : 2021-04-01 DOI: 10.1109/TSMC.2019.2912194

Jiahai Wang, Liangsheng Yuan, Zizhen Zhang, Shangce Gao, Yuyan Sun, Yalan Zhou

引用次数: 25

A Visual Leader-Following Approach With a T-D-R Framework for Quadruped Robots 基于T-D-R框架的四足机器人视觉领导跟随方法

IEEE Transactions on Systems Man and Cybernetics Part A-Systems and Humans Pub Date : 2021-04-01 DOI: 10.1109/TSMC.2019.2912715

Lei Pang, Zhiqiang Cao, Junzhi Yu, Peiyu Guan, Xuewen Rong, Hui Chai

{"title":"A Visual Leader-Following Approach With a T-D-R Framework for Quadruped Robots","authors":"Lei Pang, Zhiqiang Cao, Junzhi Yu, Peiyu Guan, Xuewen Rong, Hui Chai","doi":"10.1109/TSMC.2019.2912715","DOIUrl":"https://doi.org/10.1109/TSMC.2019.2912715","url":null,"abstract":"The quadruped robot imitates the motions of four-legged animals with a superior flexibility and adaptability to complex terrains, compared with the wheeled and tracked robots. Its leader-following ability is unique to help a human to accomplish complex tasks in a more convenient way. However, long-term following is severely obstructed due to the high-frequency vibration of the quadruped robot and the unevenness of terrains. To solve this problem, a visual approach under a novel T-D-R framework is proposed. The proposed T-D-R framework is composed of a visual tracker based on correlation filter, a person detector with deep learning, and a person re-identification (re-ID) module. The result of the tracker is verified by the detector to improve tracking performance. Especially, the re-ID module is introduced to handle distractions and occlusion caused by other persons, where the convolutional correlation filter (CCF) is employed to discriminate the leader among multiple persons through recording the appearance information in the long run. By comparing the results of the tracker and the detector as well as their similarity scores with the leader identified by the re-ID module, a stable and real-time tracking of the leader can be guaranteed. Experiments reveal that our approach is effective in handling distractions, appearance changes, and illumination variations. A long-distance experiment on a quadruped robot indicates the validity of the proposed approach.","PeriodicalId":55007,"journal":{"name":"IEEE Transactions on Systems Man and Cybernetics Part A-Systems and Humans","volume":"21 1","pages":"2342-2354"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72559117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Robust Containment Control of Uncertain Multi-Agent Systems With Time-Delay and Heterogeneous Lipschitz Nonlinearity 时滞非均质Lipschitz非线性不确定多智能体系统的鲁棒控制

IEEE Transactions on Systems Man and Cybernetics Part A-Systems and Humans Pub Date : 2021-04-01 DOI: 10.1109/TSMC.2019.2912268

Mohsen Parsa, M. Danesh

引用次数: 14

Deep Attributed Network Embedding by Preserving Structure and Attribute Information 基于结构和属性信息保持的深度属性网络嵌入

IEEE Transactions on Systems Man and Cybernetics Part A-Systems and Humans Pub Date : 2021-03-01 DOI: 10.1109/TSMC.2019.2897152

Richang Hong, Y. He, Le Wu, Yong Ge, Xindong Wu

{"title":"Deep Attributed Network Embedding by Preserving Structure and Attribute Information","authors":"Richang Hong, Y. He, Le Wu, Yong Ge, Xindong Wu","doi":"10.1109/TSMC.2019.2897152","DOIUrl":"https://doi.org/10.1109/TSMC.2019.2897152","url":null,"abstract":"Network embedding aims to learn distributed vector representations of nodes in a network. The problem of network embedding is fundamentally important. It plays crucial roles in many applications, such as node classification, link prediction, and so on. As the real-world networks are often sparse with few observed links, many recent works have utilized the local and global network structure proximity with shallow models for better network embedding. In reality, each node is usually associated with rich attributes. Some attributed network embedding models leveraged the node attributes in these shallow network embedding models to alleviate the data sparsity issue. Nevertheless, the underlying structure of the network is complex. What is more, the connection between the network structure and node attributes is also hidden. Thus, these previous shallow models fail to capture the nonlinear deep information embedded in the attributed network, resulting in the suboptimal embedding results. In this paper, we propose a deep attributed network embedding framework to capture the complex structure and attribute information. Specifically, we first adopt a personalized random walk-based model to capture the interaction between network structure and node attributes from various degrees of proximity. After that, we construct an enhanced matrix representation of the attributed network by summarizing the various degrees of proximity. Then, we design a deep neural network to exploit the nonlinear complex information in the enhanced matrix for network embedding. Thus, the proposed framework could capture the complex attributed network structure by preserving both the various degrees of network structure and node attributes in a unified framework. Finally, empirical experiments show the effectiveness of our proposed framework on a variety of network embedding-based tasks.","PeriodicalId":55007,"journal":{"name":"IEEE Transactions on Systems Man and Cybernetics Part A-Systems and Humans","volume":"17 1","pages":"1434-1445"},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74434980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 37

A Three-Level Hierarchical Graph Model for Conflict Resolution 冲突解决的三层层次图模型

IEEE Transactions on Systems Man and Cybernetics Part A-Systems and Humans Pub Date : 2021-03-01 DOI: 10.1109/TSMC.2019.2897176

Shawei He, D. Kilgour, K. Hipel

引用次数: 8

Learning Graph Similarity With Large Spectral Gap 大谱隙下的图相似度学习

IEEE Transactions on Systems Man and Cybernetics Part A-Systems and Humans Pub Date : 2021-03-01 DOI: 10.1109/TSMC.2019.2899398

Zongze Wu, Sihui Liu, C. Ding, Zhigang Ren, Shengli Xie

引用次数: 20

An Adaptive Resource Allocation Strategy for Objective Space Partition-Based Multiobjective Optimization 基于目标空间划分的多目标优化自适应资源分配策略

IEEE Transactions on Systems Man and Cybernetics Part A-Systems and Humans Pub Date : 2021-03-01 DOI: 10.1109/TSMC.2019.2898456

Huangke Chen, Guohua Wu, W. Pedrycz, P. N. Suganthan, Lining Xing, Xiaomin Zhu

{"title":"An Adaptive Resource Allocation Strategy for Objective Space Partition-Based Multiobjective Optimization","authors":"Huangke Chen, Guohua Wu, W. Pedrycz, P. N. Suganthan, Lining Xing, Xiaomin Zhu","doi":"10.1109/TSMC.2019.2898456","DOIUrl":"https://doi.org/10.1109/TSMC.2019.2898456","url":null,"abstract":"In evolutionary computation, balancing the diversity and convergence of the population for multiobjective evolutionary algorithms (MOEAs) is one of the most challenging topics. Decomposition-based MOEAs are efficient for population diversity, especially when the branch partitions the objective space of multiobjective optimization problem (MOP) into a series of subspaces, and each subspace retains a set of solutions. However, a persisting challenge is how to strengthen the population convergence while maintaining diversity for decomposition-based MOEAs. To address this issue, we first define a novel metric to measure the contributions of subspaces to the population convergence. Then, we develop an adaptive strategy that allocates computational resources to each subspace according to their contributions to the population. Based on the above two strategies, we design an objective space partition-based adaptive MOEA, called OPE-MOEA, to improve population convergence, while maintaining population diversity. Finally, 41 widely used MOP benchmarks are used to compare the performance of the proposed OPE-MOEA with other five representative algorithms. For the 41 MOP benchmarks, the OPE-MOEA significantly outperforms the five algorithms on 28 MOP benchmarks in terms of the metric hypervolume.","PeriodicalId":55007,"journal":{"name":"IEEE Transactions on Systems Man and Cybernetics Part A-Systems and Humans","volume":"13 1","pages":"1507-1522"},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88632742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 54