IEEE Transactions on Cybernetics最新文献_第10页

Safe Reinforcement Learning: Optimal Formation Control With Collision Avoidance of Multiple Satellite Systems 安全强化学习：避免多卫星系统碰撞的最佳编队控制

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2024-11-18 DOI: 10.1109/TCYB.2024.3491582

Hui Yu;Liqian Dou;Xiuyun Zhang;Jinna Li;Qun Zong

引用次数: 0

Observer-Based Human-in-the-Loop Optimal Output Cluster Synchronization Control for Multiagent Systems: A Model-Free Reinforcement Learning Method 基于观测器的多代理系统人在环最优输出簇同步控制：一种无模型强化学习方法

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2024-11-18 DOI: 10.1109/TCYB.2024.3490602

Zongsheng Huang;Tieshan Li;Yue Long;Hongjing Liang

{"title":"Observer-Based Human-in-the-Loop Optimal Output Cluster Synchronization Control for Multiagent Systems: A Model-Free Reinforcement Learning Method","authors":"Zongsheng Huang;Tieshan Li;Yue Long;Hongjing Liang","doi":"10.1109/TCYB.2024.3490602","DOIUrl":"10.1109/TCYB.2024.3490602","url":null,"abstract":"This article investigates the observer-based human-in-the-loop (HiTL) optimal output cluster synchronization control problem for nonlinear multiagent systems (MASs). First, the leader is designed to be nonautonomous, with the unknown time-varying input monitored by the human operator directly. To address the problem that leader’s output is not available to each follower, an observer is designed. This observer features practical prescribed-time convergence, and independence of prior knowledge of leader’s input. Then, an augmented system consisting of observer dynamics and follower dynamics is constructed and a cost function is formulated. Accordingly, the HiTL optimal output cluster synchronization control problem is transformed into a solution to the Hamilton-Jacobian–Bellman equation (HJBE). Subsequently, the off-policy reinforcement learning algorithm is utilized to learn the solution to HJBE without complete knowledge of the system dynamics. To alleviate computational burden, the single critic neural network (NN) is employed for the algorithm implementation, with the least square method applied for training the NN weights. Finally, the simulation results are presented to verify the validity of the designed control scheme.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 2","pages":"649-660"},"PeriodicalIF":9.4,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142670656","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}

引用次数: 0

A Hierarchical Surrogate-Assisted Differential Evolution With Core Space Localization 具有核心空间定位功能的分层代理辅助差分进化论

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2024-11-18 DOI: 10.1109/TCYB.2024.3489885

Laiqi Yu;Zhenyu Meng;Haibin Zhu

{"title":"A Hierarchical Surrogate-Assisted Differential Evolution With Core Space Localization","authors":"Laiqi Yu;Zhenyu Meng;Haibin Zhu","doi":"10.1109/TCYB.2024.3489885","DOIUrl":"10.1109/TCYB.2024.3489885","url":null,"abstract":"Surrogate-assisted evolutionary algorithms (SAEAs) are extensively used to tackle expensive optimization problems (EOPs). The integration of surrogate-based global and local search is a prevalent hierarchical SAEA framework, which can effectively balance exploration and exploitation capabilities. However, it still faces challenges when tackling high-dimensional EOPs (HEOPs) owing to the curse of dimensionality. In this article, we propose a hierarchical surrogate-assisted differential evolution with core space localization (HSADE-CS) to solve HEOPs. Its contributions are listed as follows: 1) a top-promising sampling strategy is introduced in the global search to mitigate the challenges posed by the uncertainty in the performance of the surrogate model; 2) a core space localization (CSL) method is proposed to identify a high-potential space within the local promising region, enhancing the effectiveness of local search; and 3) a fitness-independent adaptive parameter control method based on the Minkowski distance is developed within the differential evolution (DE) optimizer to improve the performance of surrogate model-driven local search. The performance of HSADE-CS has been validated on numerous benchmark problems from the commonly used expensive optimization benchmark suite, as well as the CEC2014 and CEC2017 benchmark suites, with problem dimensions up to 500. It has also been tested on a real-world problem, i.e., circular antenna array design optimization. Experimental results demonstrate that HSADE-CS is highly competitive compared to the state-of-the-art SAEAs.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 2","pages":"939-952"},"PeriodicalIF":9.4,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142670655","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}

引用次数: 0

Online Trajectory Planning Method for Autonomous Ground Vehicles Confronting Sudden and Moving Obstacles Based on LSTM-Attention Network 基于 LSTM-注意力网络的地面自主飞行器面对突发性移动障碍物时的在线轨迹规划方法

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2024-11-14 DOI: 10.1109/TCYB.2024.3486004

Zhida Xing;Runqi Chai;Kaiyuan Chen;Yuanqing Xia;Senchun Chai

{"title":"Online Trajectory Planning Method for Autonomous Ground Vehicles Confronting Sudden and Moving Obstacles Based on LSTM-Attention Network","authors":"Zhida Xing;Runqi Chai;Kaiyuan Chen;Yuanqing Xia;Senchun Chai","doi":"10.1109/TCYB.2024.3486004","DOIUrl":"10.1109/TCYB.2024.3486004","url":null,"abstract":"This article presents a novel online obstacle avoidance trajectory planning method for autonomous ground vehicles (AGVs) based on long short-term memory-attention (LSTM-Attention) networks. The proposed method can guide AGVs to perform emergency maneuvers when encountering sudden and moving obstacles, while also ensuring high levels of real-time performance and optimality. It consists of two parts: 1) offline training and 2) online planning. In the offline training phase, an AGV obstacle avoidance trajectory dataset is generated using numerical trajectory optimization methods to train the LSTM-Attention network. This training allows the network to capture the mapping between the relative information of the vehicle and the obstacles and the optimal control actions. The trained network is then used for online trajectory planning to achieve optimal feedback obstacle avoidance control for AGVs facing sudden obstacles. Furthermore, to address situations involving sudden obstacles in different directions and moving obstacles, a rotation coordinate system method is proposed, significantly expanding the application scenarios of the proposed approach. The effectiveness and real-time performance of the designed method are comprehensively validated through extensive simulation and physical experiments.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 1","pages":"421-435"},"PeriodicalIF":9.4,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142637321","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}

引用次数: 0

Data-Driven Event-Triggered Sliding Mode Secure Control for Autonomous Vehicles Under Actuator Attacks 致动器攻击下自动驾驶汽车的数据驱动事件触发式滑动模式安全控制

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2024-11-14 DOI: 10.1109/TCYB.2024.3490656

Hong-Tao Sun;Xinran Chen;Zhengqiang Zhang;Xiaohua Ge;Chen Peng

引用次数: 0

Extended Kalman Filtering-Based Nonlinear Model Predictive Control for Underactuated Systems With Multiple Constraints and Obstacle Avoidance 基于卡尔曼滤波的扩展非线性模型预测控制，适用于具有多重约束条件和避障功能的欠驱动系统

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2024-11-13 DOI: 10.1109/TCYB.2024.3488371

Meng Zhai;Tong Yang;Qingxiang Wu;Shudong Guo;Ruiping Pang;Ning Sun

{"title":"Extended Kalman Filtering-Based Nonlinear Model Predictive Control for Underactuated Systems With Multiple Constraints and Obstacle Avoidance","authors":"Meng Zhai;Tong Yang;Qingxiang Wu;Shudong Guo;Ruiping Pang;Ning Sun","doi":"10.1109/TCYB.2024.3488371","DOIUrl":"10.1109/TCYB.2024.3488371","url":null,"abstract":"Underactuated systems are a class of systems in which the number of control inputs is less than the degrees of freedom (DoFs) to be controlled. With the increasing demand for the control performance of underactuated systems, the current research on their optimization of steady-state performance is no longer sufficient. However, owing to limited control inputs, ensuring their transient performance is often difficult. Moreover, some specific composite variables in underactuated systems should be kept within the preset ranges, which poses a significant challenge to collision avoidance safety. In addition, the sensor noises are also an issue that cannot be ignored. To this end, an extended Kalman filtering-based nonlinear model predictive control method for underactuated systems is developed in this article. The key feature of this method is that it simultaneously ensures accurate positioning, multiple constraints, and obstacle avoidance. Specifically, by adding an artificial potential field as an obstacle avoidance penalty term in the cost function and dynamically assigning weight coefficients, efficient collision avoidance control is achieved. Furthermore, it is combined with the extended Kalman filtering and jointly applied to underactuated systems with sensor noises. To the best of our knowledge, it is the first control method that simultaneously considers full-state constraints, specific composite variable constraints, control input and its increment constraints, as well as obstacle avoidance in underactuated systems. The satisfactory control performance of the proposed method is validated by implementing it on two typical underactuated systems, that is, four-DoF overhead cranes and five-DoF tower cranes.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 1","pages":"369-382"},"PeriodicalIF":9.4,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142610721","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}

引用次数: 0

Self-Supervised Learning for Intuitive Control of Prosthetic Hand Movements via Sonomyography 通过超声造影进行自我监督学习以直观控制假手运动

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2024-11-13 DOI: 10.1109/TCYB.2024.3489438

Xingchen Yang;Zongtian Yin;Yixuan Sheng;Dario Farina;Honghai Liu

{"title":"Self-Supervised Learning for Intuitive Control of Prosthetic Hand Movements via Sonomyography","authors":"Xingchen Yang;Zongtian Yin;Yixuan Sheng;Dario Farina;Honghai Liu","doi":"10.1109/TCYB.2024.3489438","DOIUrl":"10.1109/TCYB.2024.3489438","url":null,"abstract":"As a primary effector of humans, the hand plays a crucial role in many aspects of daily life. Recognizing multidegree-of-freedom hand movements from muscle activity helps infer human motion intentions. Solving this problem has direct applications in prosthetic and exoskeleton control. Here, we propose a self-supervised learning algorithm inspired by muscle synergies to achieve simultaneous estimation of wrist rotation (supination/pronation) and hand grasp (open/close) from sonomyography—the muscle deformation detected by a wearable ultrasound array. Unlike conventional methods collecting both muscle activity and hand kinematics for supervised model calibration, this algorithm only uses unlabeled forearm ultrasound signals for self-supervised wrist and hand movement estimation, where movement labels are auto-generated. The performance of the proposed algorithm was experimentally evaluated with ten participants including an amputee. Offline analysis demonstrated that the proposed algorithm can accurately estimate simultaneous wrist rotation and hand grasp movements (\u0000<inline-formula> <tex-math>$r_{textrm {wrist}}$ </tex-math></inline-formula>\u0000 and \u0000<inline-formula> <tex-math>$r_{textrm {hand}}$ </tex-math></inline-formula>\u0000 were 0.98 and 0.94 for the able-bodied, and 0.98 and 0.90 for the amputee, respectively). Notably, the performance of the self-supervised learning was superior to the supervised learning for the amputee. Online experiments demonstrated that intended wrist and hand movements can be deciphered in real time, enabling accurate control of a virtual hand. This study will open up a new avenue for the sonomyographic human-machine interaction.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 1","pages":"409-420"},"PeriodicalIF":9.4,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142610701","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}

引用次数: 0

Periodic Event-Triggered Optimal Output Consensus of Heterogeneous Multiagent Systems Subject to Communication Delays 受通信延迟影响的异构多代理系统的周期性事件触发最佳输出共识

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2024-11-13 DOI: 10.1109/TCYB.2024.3485230

Tianyu Liu;Lu Liu

引用次数: 0

Uncovering Reward Goals in Distributed Drone Swarms Using Physics-Informed Multiagent Inverse Reinforcement Learning 利用物理信息多代理反强化学习在分布式无人机群中发现奖励目标

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2024-11-13 DOI: 10.1109/TCYB.2024.3489967

Adolfo Perrusquía;Weisi Guo

{"title":"Uncovering Reward Goals in Distributed Drone Swarms Using Physics-Informed Multiagent Inverse Reinforcement Learning","authors":"Adolfo Perrusquía;Weisi Guo","doi":"10.1109/TCYB.2024.3489967","DOIUrl":"10.1109/TCYB.2024.3489967","url":null,"abstract":"The cooperative nature of drone swarms poses risks in the smooth operation of services and the security of national facilities. The control objective of the swarm is, in most cases, occluded due to the complex behaviors observed in each drone. It is paramount to understand which is the control objective of the swarm, whilst understanding better how they communicate with each other to achieve the desired task. To solve these issues, this article proposes a physics-informed multiagent inverse reinforcement learning (PI-MAIRL) that: 1) infers the control objective function or reward function from observational data and 2) uncover the network topology by exploiting a physics-informed model of the dynamics of each drone. The combined contribution enables to understand better the behavior of the swarm, whilst enabling the inference of its objective for experience inference and imitation learning. A physically uncoupled swarm scenario is considered in this study. The incorporation of the physics-informed element allows to obtain an algorithm that is computationally more efficient than model-free IRL algorithms. Convergence of the proposed approach is verified using Lyapunov recursions on a global Riccati equation. Simulation studies are carried out to show the benefits and challenges of the approach.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 1","pages":"14-23"},"PeriodicalIF":9.4,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142610702","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}

引用次数: 0

Task-Oriented Tool Manipulation With Robotic Dexterous Hands: A Knowledge Graph Approach From Fingers to Functionality 以任务为导向的机器人灵巧手工具操纵：从手指到功能的知识图谱方法

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2024-11-13 DOI: 10.1109/TCYB.2024.3487845

Fan Yang;Wenrui Chen;Haoran Lin;Sijie Wu;Xin Li;Zhiyong Li;Yaonan Wang

{"title":"Task-Oriented Tool Manipulation With Robotic Dexterous Hands: A Knowledge Graph Approach From Fingers to Functionality","authors":"Fan Yang;Wenrui Chen;Haoran Lin;Sijie Wu;Xin Li;Zhiyong Li;Yaonan Wang","doi":"10.1109/TCYB.2024.3487845","DOIUrl":"10.1109/TCYB.2024.3487845","url":null,"abstract":"A primary challenge in robotic tool use is achieving precise manipulation with dexterous robotic hands to mimic human actions. It requires understanding human tool use and allocating specific functions to each robotic finger for fine control. Existing work has primarily focused on the overall grasping capabilities of robotic hands, often neglecting the functional allocation among individual fingers during object interaction. In response to this, we introduce a semantic knowledge-driven approach to distribute functions among fingers for tool manipulation. Central to this approach is the finger-to-function (F2F) knowledge graph, which captures human expertise in tool use and establishes relationships between tool attributes, tasks, and manipulation elements, including functional fingers, components, required force, and gestures. We also develop a manipulation element-oriented prediction algorithm using knowledge graph semantic embedding, enhancing the prediction of manipulation elements’ speed and accuracy. Additionally, we propose the functionality-integrated adaptive force feedback manipulation (FAFM) module, which integrates manipulation elements with adaptive force feedback to achieve precise finger-level control. Our framework does not rely on extensive annotated data for supervision but utilizes semantic constraints from F2F to guide tool manipulation. The proposed method demonstrates superior performance and generalizability in real-world scenarios, achieving an 8% higher success rate in grasping and manipulation of representative tool instances compared to the existing state-of-the-art methods. The dataset and code are available at \u0000<uri>https://github.com/yangfan293/F2F</uri>\u0000.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 1","pages":"395-408"},"PeriodicalIF":9.4,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142610705","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}

引用次数: 0