IEEE Transactions on Systems Man and Cybernetics Part A-Systems and Humans最新文献_第7页

Adaptive NN Cooperative Control of Unknown Nonlinear Multiagent Systems With Communication Delays 具有通信延迟的未知非线性多智能体系统的自适应神经网络协同控制

IEEE Transactions on Systems Man and Cybernetics Part A-Systems and Humans Pub Date : 2021-09-01 DOI: 10.1109/TSMC.2019.2950114

H. E. Psillakis

引用次数: 12

Estimation for Networked Random Sampling Systems With Packet Losses 具有丢包的网络随机抽样系统的估计

IEEE Transactions on Systems Man and Cybernetics Part A-Systems and Humans Pub Date : 2021-09-01 DOI: 10.1109/TSMC.2019.2956156

Honglei Lin, Shuli Sun

引用次数: 2

Self-Attention-Based Temporary Curiosity in Reinforcement Learning Exploration 强化学习探索中基于自我注意的暂时好奇心

IEEE Transactions on Systems Man and Cybernetics Part A-Systems and Humans Pub Date : 2021-09-01 DOI: 10.1109/TSMC.2019.2957051

Hangkai Hu, Shiji Song, Gao Huang

{"title":"Self-Attention-Based Temporary Curiosity in Reinforcement Learning Exploration","authors":"Hangkai Hu, Shiji Song, Gao Huang","doi":"10.1109/TSMC.2019.2957051","DOIUrl":"https://doi.org/10.1109/TSMC.2019.2957051","url":null,"abstract":"In many real-world scenarios, extrinsic rewards provided by the environment are sparse. An agent trained with classic reinforcement learning algorithm fails to explore these environments in a sufficient and effective way. To address this problem, the exploration bonus which derives from environmental novelty serves as intrinsic motivation for the agent. In recent years, curiosity-driven exploration is a mainstream approach to describe environmental novelty through prediction errors of dynamics models. Due to the expressive ability limitations of curiosity-based environmental novelty and the difficulty of finding appropriate feature space, most curiosity-driven exploration methods have the problem of overprotection against repetition. This problem can reduce the efficiency of exploration and lead the agent into a trap with local optimality. In this article, we propose a combination of persisting curiosity and temporary curiosity framework to deal with the problem of overprotection against repetition. We introduce the self-attention mechanism from the field of computer vision and propose a sequence-based self-attention mechanism for temporary curiosity generation. We compare our framework with some previous exploration methods in hard-exploration environments, provide a series of comprehensive analysis of the proposed framework and investigate the effect of the individual components of our method. The experimental results indicate that the proposed framework delivers superior performance than existing methods.","PeriodicalId":55007,"journal":{"name":"IEEE Transactions on Systems Man and Cybernetics Part A-Systems and Humans","volume":"41 1","pages":"5773-5784"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77543648","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: 5

New Joint-Drift-Free Scheme Aided with Projected ZNN for Motion Generation of Redundant Robot Manipulators Perturbed by Disturbances 基于投影ZNN的冗余机器人运动生成联合无漂移新方案

IEEE Transactions on Systems Man and Cybernetics Part A-Systems and Humans Pub Date : 2021-09-01 DOI: 10.1109/tsmc.2019.2956961

Huiyan Lu, Long Jin, Jiliang Zhang, Zhenan Sun, Shuai Li, Zhijun Zhang

{"title":"New Joint-Drift-Free Scheme Aided with Projected ZNN for Motion Generation of Redundant Robot Manipulators Perturbed by Disturbances","authors":"Huiyan Lu, Long Jin, Jiliang Zhang, Zhenan Sun, Shuai Li, Zhijun Zhang","doi":"10.1109/tsmc.2019.2956961","DOIUrl":"https://doi.org/10.1109/tsmc.2019.2956961","url":null,"abstract":"Joint-drift problems could result in failures in executing task or even damage robots in actual applications and different schemes have been presented to deal with such a knotty problem. However, in these existing schemes, there exists the coupling in coefficients for eliminating the drift in the joint space and the equality constraint for completing the given task in the Cartesian space, thereby, theoretically, leading to a paradox in achieving zero joint drift in the joint space and zero position error in the Cartesian space simultaneously. A novel joint-drift-free (JDF) scheme synthesized by a projected zeroing neural network (PZNN) model for the motion generation and control of redundant robot manipulators perturbed by disturbances is proposed and analyzed in this article. Besides, the PZNN model could adopt saturated or even nonconvex projection functions. The proposed scheme completely decouples the interferences of joint errors in the joint space and position errors in the Cartesian space for the first time. Beyond that, theoretical analysis is conducted in order to validate that the PZNN model is of global convergence to the theoretical kinematics solution to the motion generation of robots, and that the joint-drift problems are thus remedied. Moreover, several simulations and physical experiments on the strength of different robot manipulators are carried out to confirm the superiority, efficiency, and accuracy of the proposed JDF scheme synthesized by the PZNN model for remedying joint-drift problems of redundant robot manipulators in noisy environments.","PeriodicalId":55007,"journal":{"name":"IEEE Transactions on Systems Man and Cybernetics Part A-Systems and Humans","volume":"37 1","pages":"5639-5651"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83847084","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: 30

Distributed Containment Control of MIMO Pure-Feedback Multiagent Systems Using Filter-Driven-Approximation Approach 基于滤波驱动逼近的MIMO纯反馈多智能体系统分布式控制

IEEE Transactions on Systems Man and Cybernetics Part A-Systems and Humans Pub Date : 2021-09-01 DOI: 10.1109/TSMC.2019.2955996

Yun Ho Choi, S. Yoo

{"title":"Distributed Containment Control of MIMO Pure-Feedback Multiagent Systems Using Filter-Driven-Approximation Approach","authors":"Yun Ho Choi, S. Yoo","doi":"10.1109/TSMC.2019.2955996","DOIUrl":"https://doi.org/10.1109/TSMC.2019.2955996","url":null,"abstract":"This article addresses a filter-driven-approximation (FDA)-based design problem for the distributed containment control of multi-input–multi-output pure-feedback multiagent systems with completely unknown nonlinearities. Local filter-driven approximators are designed to compensate for unknown nonaffine nonlinear functions lumped in the local controller design procedure where the first-order filtered signals of the error surfaces, state variables, and control inputs are linearly combined for the design of the filter-driven approximators. A containment control scheme using the filter-driven function approximators is recursively constructed to ensure that the outputs of the followers converge to the convex hull spanned by multiple time-varying leaders. Compared with existing containment control results using adaptive neural-network-based or fuzzy-based approximators, the proposed FDA-based containment control scheme depends only on the relative output information among agents and does not require any adaptive techniques. Thus, the proposed control structure can be simplified. It is shown that the closed-loop signals, including approximation errors are semi-globally uniformly ultimately bounded. Simulation examples are provided to validate the effectiveness of the proposed theoretical strategy.","PeriodicalId":55007,"journal":{"name":"IEEE Transactions on Systems Man and Cybernetics Part A-Systems and Humans","volume":"IA-21 1","pages":"5490-5502"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84609215","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: 3

Semantic-Aware Informative Path Planning for Efficient Object Search Using Mobile Robot 基于语义感知的移动机器人高效目标搜索信息路径规划

IEEE Transactions on Systems Man and Cybernetics Part A-Systems and Humans Pub Date : 2021-08-01 DOI: 10.1109/TSMC.2019.2946646

Chaoqun Wang, Jiyu Cheng, Wenzheng Chi, Tingfang Yan, M. Meng

{"title":"Semantic-Aware Informative Path Planning for Efficient Object Search Using Mobile Robot","authors":"Chaoqun Wang, Jiyu Cheng, Wenzheng Chi, Tingfang Yan, M. Meng","doi":"10.1109/TSMC.2019.2946646","DOIUrl":"https://doi.org/10.1109/TSMC.2019.2946646","url":null,"abstract":"In this article, a novel informative path planning (IPP) framework is proposed for efficient robotic object search. We innovatively reformulate the object search into an IPP problem, which takes account of the knowledge of possible target object locations. To model the target object distribution knowledge, the semantic information of the focused environment is utilized to obtain the probabilities of finding the target object at possible locations. Then, the probability distribution is modeled by Gaussian mixture model (GMM) to generate an information map. Based on the map, a sampling-based IPP method is proposed to minimize the object search cost. It is worth noting that the object search path is planned with a tree structure and evaluated by a utility function that concerns both search information gain and path cost. Moreover, to improve the quality of the search path, a novel informative sampling strategy and a rewire mechanism are conceived. The performance of the proposed object search framework is fully evaluated through both simulation experiments and real-world tests with a mobile robot platform. Results demonstrated that our method can find the target object efficiently and robustly with shorter path length than three comparative methods in the literature and the mobile robot shows human-like behavior when searching for the target object.","PeriodicalId":55007,"journal":{"name":"IEEE Transactions on Systems Man and Cybernetics Part A-Systems and Humans","volume":"59 1","pages":"5230-5243"},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81237946","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: 14

A Unified Scheme for Distance Metric Learning and Clustering via Rank-Reduced Regression 基于秩降回归的距离度量学习和聚类的统一方案

IEEE Transactions on Systems Man and Cybernetics Part A-Systems and Humans Pub Date : 2021-08-01 DOI: 10.1109/TSMC.2019.2946398

Wenzhong Guo, Yiqing Shi, Shiping Wang

{"title":"A Unified Scheme for Distance Metric Learning and Clustering via Rank-Reduced Regression","authors":"Wenzhong Guo, Yiqing Shi, Shiping Wang","doi":"10.1109/TSMC.2019.2946398","DOIUrl":"https://doi.org/10.1109/TSMC.2019.2946398","url":null,"abstract":"Distance metric learning aims to learn a positive semidefinite matrix such that similar samples are preserved with small distances while dissimilar ones are mapped with big values above a predefined margin. It can facilitate to improve the performance of certain learning tasks. In this article, distance metric learning and clustering are integrated into an unified framework via rank-reduced regression. First, distance metric learning is proved to be consistent with rank-reduced regression, which provides a new perspective to learn structured regularization matrices. Second, orthogonal and non-negative rank-reduced regression problems are addressed individually for clustering, and the corresponding algorithms with proved convergence are proposed. Finally, both distance metric learning and clustering are addressed simultaneously in the problem formulation, which may trigger some new insights for learning an effective clustering oriented low-dimensional embedding. To show the superior performance of the proposed method, we compare it with several state-of-the-art clustering approaches. And, extensive experiments on the test datasets demonstrate the superiority of the proposed method.","PeriodicalId":55007,"journal":{"name":"IEEE Transactions on Systems Man and Cybernetics Part A-Systems and Humans","volume":"16 1","pages":"5218-5229"},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78848489","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: 7

Finite-Time Synchronization of Fractional-Order Complex-Variable Dynamic Networks 分数阶复变动态网络的有限时间同步

IEEE Transactions on Systems Man and Cybernetics Part A-Systems and Humans Pub Date : 2021-07-01 DOI: 10.1109/TSMC.2019.2931339

Tianqi Hou, Juan Yu, Cheng Hu, Haijun Jiang

引用次数: 33

High-Precision Attitude Tracking Control of Space Manipulator System Under Multiple Disturbances 多干扰下空间机械臂系统的高精度姿态跟踪控制

IEEE Transactions on Systems Man and Cybernetics Part A-Systems and Humans Pub Date : 2021-07-01 DOI: 10.1109/TSMC.2019.2931930

Jianzhong Qiao, Hao Wu, Xiang Yu

{"title":"High-Precision Attitude Tracking Control of Space Manipulator System Under Multiple Disturbances","authors":"Jianzhong Qiao, Hao Wu, Xiang Yu","doi":"10.1109/TSMC.2019.2931930","DOIUrl":"https://doi.org/10.1109/TSMC.2019.2931930","url":null,"abstract":"Precise attitude control of space manipulators plays an important role in advanced on-orbit assembly tasks. The vibration of flexible appendage and inertial uncertainties encountered in the operating process, however, may cause attitude error or even safety threats to the space manipulator system. In this article, a high-precision attitude control scheme of a space manipulator system is designed via a combination of disturbance observer (DO), prescribed performance-based <inline-formula> <tex-math notation=\"LaTeX\">${H} _{infty }$ </tex-math></inline-formula> control, and iterative learning control (ILC) techniques. The proposed control scheme consists of three portions: 1) a DO that estimates the vibration disturbance caused by flexible appendage of base satellite; 2) a robust <inline-formula> <tex-math notation=\"LaTeX\">${H} _{infty }$ </tex-math></inline-formula> controller with prescribed performance to attenuate the inertial uncertainties resulting from capture of an unknown object; and 3) an ILC for improving the transient and steady-state process in the presence of a repetitive on-orbit assembly task. This novel control scheme can not only handle the flexible vibration and inertial uncertainty of the space manipulator but also achieve satisfactory tracking performance. Both simulation and experimental results confirm the superiority of the proposed control strategy.","PeriodicalId":55007,"journal":{"name":"IEEE Transactions on Systems Man and Cybernetics Part A-Systems and Humans","volume":"3 1","pages":"4274-4284"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74870579","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: 12

IEEE Systems, Man, and Cybernetics Society Information IEEE系统、人与控制论学会信息

IEEE Transactions on Systems Man and Cybernetics Part A-Systems and Humans Pub Date : 2021-07-01 DOI: 10.1109/tsmc.2021.3086600

引用次数: 0