Pub Date : 2024-12-17DOI: 10.1109/TSMC.2024.3517097
{"title":"IEEE Transactions on Systems, Man, and Cybernetics: Systems Information for Authors","authors":"","doi":"10.1109/TSMC.2024.3517097","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3517097","url":null,"abstract":"","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 1","pages":"C4-C4"},"PeriodicalIF":8.6,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10805222","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-17DOI: 10.1109/TSMC.2024.3517095
{"title":"IEEE Systems, Man, and Cybernetics Society Information","authors":"","doi":"10.1109/TSMC.2024.3517095","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3517095","url":null,"abstract":"","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 1","pages":"C3-C3"},"PeriodicalIF":8.6,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10805232","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-17DOI: 10.1109/TSMC.2024.3517093
{"title":"IEEE Transactions on Systems, Man, and Cybernetics: Systems Publication Information","authors":"","doi":"10.1109/TSMC.2024.3517093","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3517093","url":null,"abstract":"","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 1","pages":"C2-C2"},"PeriodicalIF":8.6,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10805227","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-17DOI: 10.1109/TSMC.2024.3514435
{"title":"IEEE Transactions on Systems, Man, and Cybernetics: Systems Publication Information","authors":"","doi":"10.1109/TSMC.2024.3514435","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3514435","url":null,"abstract":"","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 1","pages":"C2-C2"},"PeriodicalIF":8.6,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10805225","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-17DOI: 10.1109/TSMC.2024.3514444
{"title":"TechRxiv: Share Your Preprint Research With the World!","authors":"","doi":"10.1109/TSMC.2024.3514444","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3514444","url":null,"abstract":"","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 1","pages":"414-414"},"PeriodicalIF":8.6,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10805223","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-12DOI: 10.1109/TSMC.2024.3507827
Chen Tang;Fuyuan Xiao
In the era of complex data environments, accurately measuring uncertainty is crucial for effective decision making. Complex evidence theory (CET) provides a framework for handling uncertainty reasoning in the complex plane. Within CET, complex basic belief assignment (CBBA) aims to tackle the uncertainty and imprecision inherent in data coinciding with phase or periodic changes. However, measuring the uncertainty of CBBA over time remains an open issue. This study introduces a novel entropy model, the complex belief (CB) entropy, within the framework of CET, designed to tackle the inherent uncertainty and imprecision in data with phase or periodic changes. The model is developed by integrating concepts of interference and fractal theory to extend the understanding of uncertainty over time. Methodologically, the CB entropy is constructed to include discord, nonspecificity, and an interaction term for focal elements, defined as interference. In addition, thanks to the concept of the fractal, the model is further generalized to time fractal-based CB (TFCB) entropy for forecasting future uncertainties. We furthermore analyze the properties of the entropy models. Findings demonstrate that the proposed entropy models provide a more comprehensive measure of uncertainty in complex scenarios. Finally, a decision-making method based on the proposed entropy is proposed.
{"title":"A Time Fractal-Based Complex Belief Entropy in Complex Evidence Theory for Pattern Classification","authors":"Chen Tang;Fuyuan Xiao","doi":"10.1109/TSMC.2024.3507827","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3507827","url":null,"abstract":"In the era of complex data environments, accurately measuring uncertainty is crucial for effective decision making. Complex evidence theory (CET) provides a framework for handling uncertainty reasoning in the complex plane. Within CET, complex basic belief assignment (CBBA) aims to tackle the uncertainty and imprecision inherent in data coinciding with phase or periodic changes. However, measuring the uncertainty of CBBA over time remains an open issue. This study introduces a novel entropy model, the complex belief (CB) entropy, within the framework of CET, designed to tackle the inherent uncertainty and imprecision in data with phase or periodic changes. The model is developed by integrating concepts of interference and fractal theory to extend the understanding of uncertainty over time. Methodologically, the CB entropy is constructed to include discord, nonspecificity, and an interaction term for focal elements, defined as interference. In addition, thanks to the concept of the fractal, the model is further generalized to time fractal-based CB (TFCB) entropy for forecasting future uncertainties. We furthermore analyze the properties of the entropy models. Findings demonstrate that the proposed entropy models provide a more comprehensive measure of uncertainty in complex scenarios. Finally, a decision-making method based on the proposed entropy is proposed.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 2","pages":"1175-1188"},"PeriodicalIF":8.6,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-12DOI: 10.1109/TSMC.2024.3509498
Guangxu He;Jun Zhao
This article investigates the cooperative output regulation problem via bumpless transfer (BT) control for switched multiagent systems. First of all, a novel distributed estimator is constructed to track the exosystem for each agent via dynamic output feedback and aperiodic sampled-data transmission. Through the time-dependent Lyapunov functional and free-weighed matrix, the tracking performance between the estimator and the exosystem is achieved and auxiliary dynamic variable asymptotically converges to zero. Further, according to the information of observer and estimator for each agent, the BT controller and the agent-state-dependent switching law are jointly designed to suppress control bumps and determine which subsystem to be activated. Besides, to cope with the tough scenario that the classical design of a single controller for each subsystem cannot achieve the decline of Lyapunov function and satisfactory BT performance simultaneously, two controllers are designed for each subsystem. In this way, a two-layer switching strategy is presented to deal with the problem of cooperative output regulation with satisfactory BT performance. Finally, two examples are provided to demonstrate the validity of results.
{"title":"Cooperative Output Regulation via Bumpless Transfer Control for Switched Multiagent Systems Under Dynamic Output Feedback","authors":"Guangxu He;Jun Zhao","doi":"10.1109/TSMC.2024.3509498","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3509498","url":null,"abstract":"This article investigates the cooperative output regulation problem via bumpless transfer (BT) control for switched multiagent systems. First of all, a novel distributed estimator is constructed to track the exosystem for each agent via dynamic output feedback and aperiodic sampled-data transmission. Through the time-dependent Lyapunov functional and free-weighed matrix, the tracking performance between the estimator and the exosystem is achieved and auxiliary dynamic variable asymptotically converges to zero. Further, according to the information of observer and estimator for each agent, the BT controller and the agent-state-dependent switching law are jointly designed to suppress control bumps and determine which subsystem to be activated. Besides, to cope with the tough scenario that the classical design of a single controller for each subsystem cannot achieve the decline of Lyapunov function and satisfactory BT performance simultaneously, two controllers are designed for each subsystem. In this way, a two-layer switching strategy is presented to deal with the problem of cooperative output regulation with satisfactory BT performance. Finally, two examples are provided to demonstrate the validity of results.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 2","pages":"1189-1200"},"PeriodicalIF":8.6,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-11DOI: 10.1109/TSMC.2024.3506587
Liangze Jiang;Zheng-Guang Wu;Lei Wang
This article studies distributed continuous-time optimization for time-varying quadratic cost functions with uncertain parameters. We first propose a centralized adaptive optimization algorithm using partial information of the cost function. It can be seen that even if there are uncertain parameters in the cost function, exact optimization can still be achieved. To solve this problem in a distributed manner when different local cost functions have identical Hessians, we propose a novel distributed algorithm that cascades the fixed-time average estimator and the distributed optimizer. We remove the requirement for the upper bounds of certain complex functions by integrating state-based gains in the proposed design. We further extend this result to address the distributed optimization where the time-varying cost functions have nonidentical Hessians. We prove the convergence of all the proposed algorithms in the global sense. Numerical examples verify the proposed algorithms.
{"title":"Distributed Continuous-Time Optimization With Uncertain Time-Varying Quadratic Cost Functions","authors":"Liangze Jiang;Zheng-Guang Wu;Lei Wang","doi":"10.1109/TSMC.2024.3506587","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3506587","url":null,"abstract":"This article studies distributed continuous-time optimization for time-varying quadratic cost functions with uncertain parameters. We first propose a centralized adaptive optimization algorithm using partial information of the cost function. It can be seen that even if there are uncertain parameters in the cost function, exact optimization can still be achieved. To solve this problem in a distributed manner when different local cost functions have identical Hessians, we propose a novel distributed algorithm that cascades the fixed-time average estimator and the distributed optimizer. We remove the requirement for the upper bounds of certain complex functions by integrating state-based gains in the proposed design. We further extend this result to address the distributed optimization where the time-varying cost functions have nonidentical Hessians. We prove the convergence of all the proposed algorithms in the global sense. Numerical examples verify the proposed algorithms.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 2","pages":"1526-1536"},"PeriodicalIF":8.6,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-11DOI: 10.1109/TSMC.2024.3495718
Zhaoyu Xiang;Yufeng Chen;Naiqi Wu;Zhiwu Li
This article investigates the state estimation for a networked timed discrete event system, where a plant communicates with a supervisor via a multichannel network characterized by bounded delays and losses. To address delays and losses in observation channels, we augment the plant by integrating the dynamics of these channels, thus capturing the system’s open-loop behavior. To tackle delays and losses in control channels, we augment the supervisor by considering all control decisions with potential impact on the system’s behavior. By integrating the augmented plant and supervisor, we introduce a compensated system that enables the derivation of an over-approximation of the closed-loop system’s behavior. Ultimately, we devise an online over-approximation state estimation algorithm for the closed-loop system, to compute all possible system states under communication delays and losses. We provide a simulation example to illustrate the efficacy of the proposed method.
{"title":"Over-Approximation State Estimation for Networked Timed Discrete Event Systems With Communication Delays and Losses","authors":"Zhaoyu Xiang;Yufeng Chen;Naiqi Wu;Zhiwu Li","doi":"10.1109/TSMC.2024.3495718","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3495718","url":null,"abstract":"This article investigates the state estimation for a networked timed discrete event system, where a plant communicates with a supervisor via a multichannel network characterized by bounded delays and losses. To address delays and losses in observation channels, we augment the plant by integrating the dynamics of these channels, thus capturing the system’s open-loop behavior. To tackle delays and losses in control channels, we augment the supervisor by considering all control decisions with potential impact on the system’s behavior. By integrating the augmented plant and supervisor, we introduce a compensated system that enables the derivation of an over-approximation of the closed-loop system’s behavior. Ultimately, we devise an online over-approximation state estimation algorithm for the closed-loop system, to compute all possible system states under communication delays and losses. We provide a simulation example to illustrate the efficacy of the proposed method.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 2","pages":"1215-1229"},"PeriodicalIF":8.6,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-11DOI: 10.1109/TSMC.2024.3501318
Yifan Wu;Yingru Sun;Xinbo Yu;Dawei Zhang;Wei He
This article presents an intelligent robotic system developed for experiments in the materials science laboratory, specifically focusing on coating preparation via layer-by-layer self-assembly techniques and hydrophobic detection. The system integrates two collaborative robotic arms, enhanced with dynamic movement primitives (DMPs), to mimic human manipulation skills and bolster the robots’ imitation capabilities. Additionally, a mobile robotic arm facilitates autonomous operations. A key component is an independently designed optical detection device capable of measuring water droplet angles. Coupled with a compatible simulation platform, the system can perform virtual experiments and generate trajectories for obstacle avoidance, and in which generative adversarial imitation learning (GAIL) in simulating robot trajectories. This article details the system’s construction, process design encompassing the robotic systems, optical detection device, simulation, and visualization platform. It also explores the vast potential of future AI-driven laboratories in materials science, biology, medicine, and chemistry.
{"title":"Intelligent Experiment Robotic Systems Design for Material Preparation and Detection","authors":"Yifan Wu;Yingru Sun;Xinbo Yu;Dawei Zhang;Wei He","doi":"10.1109/TSMC.2024.3501318","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3501318","url":null,"abstract":"This article presents an intelligent robotic system developed for experiments in the materials science laboratory, specifically focusing on coating preparation via layer-by-layer self-assembly techniques and hydrophobic detection. The system integrates two collaborative robotic arms, enhanced with dynamic movement primitives (DMPs), to mimic human manipulation skills and bolster the robots’ imitation capabilities. Additionally, a mobile robotic arm facilitates autonomous operations. A key component is an independently designed optical detection device capable of measuring water droplet angles. Coupled with a compatible simulation platform, the system can perform virtual experiments and generate trajectories for obstacle avoidance, and in which generative adversarial imitation learning (GAIL) in simulating robot trajectories. This article details the system’s construction, process design encompassing the robotic systems, optical detection device, simulation, and visualization platform. It also explores the vast potential of future AI-driven laboratories in materials science, biology, medicine, and chemistry.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 2","pages":"1241-1251"},"PeriodicalIF":8.6,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142992895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：