Pub Date : 2024-11-13DOI: 10.1109/TCYB.2024.3485230
Tianyu Liu;Lu Liu
This article investigates periodic event-triggered optimal output consensus of heterogeneous linear multiagent systems where each agent has knowledge of only its own cost function. In contrast to existing results, we consider communication delays and general strongly connected digraphs. A novel periodic event-triggered distributed control scheme is proposed, which allows asynchronous event detection and time-varying communication delays. Sufficient conditions with respect to the maximum allowable communication delays and event detection periods to achieve asymptotic optimal output consensus are established. Moreover, it is proved that the proposed periodic event-triggering mechanism can provide a positive lower bound of interevent times which is independent of the event detection period. A simulation example is provided to illustrate the effectiveness of the proposed control scheme.
{"title":"Periodic Event-Triggered Optimal Output Consensus of Heterogeneous Multiagent Systems Subject to Communication Delays","authors":"Tianyu Liu;Lu Liu","doi":"10.1109/TCYB.2024.3485230","DOIUrl":"10.1109/TCYB.2024.3485230","url":null,"abstract":"This article investigates periodic event-triggered optimal output consensus of heterogeneous linear multiagent systems where each agent has knowledge of only its own cost function. In contrast to existing results, we consider communication delays and general strongly connected digraphs. A novel periodic event-triggered distributed control scheme is proposed, which allows asynchronous event detection and time-varying communication delays. Sufficient conditions with respect to the maximum allowable communication delays and event detection periods to achieve asymptotic optimal output consensus are established. Moreover, it is proved that the proposed periodic event-triggering mechanism can provide a positive lower bound of interevent times which is independent of the event detection period. A simulation example is provided to illustrate the effectiveness of the proposed control scheme.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 1","pages":"355-368"},"PeriodicalIF":9.4,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142610700","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 : 2024-11-13DOI: 10.1109/TCYB.2024.3489967
Adolfo Perrusquía;Weisi Guo
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.
{"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}
Pub Date : 2024-11-13DOI: 10.1109/TCYB.2024.3487845
Fan Yang;Wenrui Chen;Haoran Lin;Sijie Wu;Xin Li;Zhiyong Li;Yaonan Wang
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 https://github.com/yangfan293/F2F .
{"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}
Pub Date : 2024-11-13DOI: 10.1109/TCYB.2024.3487480
Wang Yang;Jiuxiang Dong
This article addresses the output consensus problem of heterogeneous multiagent systems (MASs) via a distributed modified dynamic event-triggered control design. First, a fully distributed asynchronous event-triggered compensator for each agent is proposed to act as a virtual reference generator without using global information. Second, based on this compensator, a distributed event-triggered controller for each heterogeneous agent is developed to reach output consensus. Specifically, to save resources and guarantee asymptotic consensus, a modified dynamic event-triggered mechanism (DETM) is proposed, in which a time-triggered mechanism (TTM) is introduced to ensure the existence of a positive minimum interevent time and a DETM is designed to further avoid redundant triggering actions. Besides, the triggering condition of the DETM does not require continuous detection, it is only detected when the timer variable in the TTM deceases from maximum value to zero. These features of the developed control scheme are presented analytically and verified numerically.
{"title":"A Modified Dynamic Event-Triggered Mechanism for Output Consensus of Heterogeneous MASs","authors":"Wang Yang;Jiuxiang Dong","doi":"10.1109/TCYB.2024.3487480","DOIUrl":"10.1109/TCYB.2024.3487480","url":null,"abstract":"This article addresses the output consensus problem of heterogeneous multiagent systems (MASs) via a distributed modified dynamic event-triggered control design. First, a fully distributed asynchronous event-triggered compensator for each agent is proposed to act as a virtual reference generator without using global information. Second, based on this compensator, a distributed event-triggered controller for each heterogeneous agent is developed to reach output consensus. Specifically, to save resources and guarantee asymptotic consensus, a modified dynamic event-triggered mechanism (DETM) is proposed, in which a time-triggered mechanism (TTM) is introduced to ensure the existence of a positive minimum interevent time and a DETM is designed to further avoid redundant triggering actions. Besides, the triggering condition of the DETM does not require continuous detection, it is only detected when the timer variable in the TTM deceases from maximum value to zero. These features of the developed control scheme are presented analytically and verified numerically.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 1","pages":"383-394"},"PeriodicalIF":9.4,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142610703","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 : 2024-11-11DOI: 10.1109/TCYB.2024.3485986
Lili Wang;Shiming Chen
The leader-following consensus issue for general linear multiagent systems (MASs) is investigated under event-triggered communication (ETC). Based on the output measurement of agent, two novel adaptive dynamic event-triggered (ADET) strategies for synchronous and asynchronous ETC are proposed, in which adaptive triggering parameters and time-varying threshold are introduced. Simultaneously, the corresponding control protocols are developed under the ADET strategies. Different from most existing relevant results, triggering mechanisms and control protocols do not require global information and network size, ensuring a fully distributed implementation. The asynchronous ETC, in particular, gives a flexible for transmitting information. The effectiveness of ADET strategies is further analyzed and discussed, and a comparative analysis between observer-based and state-based ADET algorithms is provided, highlighting their efficiencies and applications. Theoretical claims are substantiated with a simulative example that demonstrates the practical effectiveness of the proposed strategies.
{"title":"Fully Distributed Observer-Based Leader-Following Consensus of Linear Multiagent Systems by Adaptive Dynamic Event-Triggered Schemes","authors":"Lili Wang;Shiming Chen","doi":"10.1109/TCYB.2024.3485986","DOIUrl":"10.1109/TCYB.2024.3485986","url":null,"abstract":"The leader-following consensus issue for general linear multiagent systems (MASs) is investigated under event-triggered communication (ETC). Based on the output measurement of agent, two novel adaptive dynamic event-triggered (ADET) strategies for synchronous and asynchronous ETC are proposed, in which adaptive triggering parameters and time-varying threshold are introduced. Simultaneously, the corresponding control protocols are developed under the ADET strategies. Different from most existing relevant results, triggering mechanisms and control protocols do not require global information and network size, ensuring a fully distributed implementation. The asynchronous ETC, in particular, gives a flexible for transmitting information. The effectiveness of ADET strategies is further analyzed and discussed, and a comparative analysis between observer-based and state-based ADET algorithms is provided, highlighting their efficiencies and applications. Theoretical claims are substantiated with a simulative example that demonstrates the practical effectiveness of the proposed strategies.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 1","pages":"332-342"},"PeriodicalIF":9.4,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142599356","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}
This article primarily studies the adaptive output-feedback consensus tracking control issue for nonlinear multiagent systems (MASs) with unknown control directions against deception attacks. First, a composite observer combining the state observer and the disturbance observer is developed to concurrently estimate the states of confronting deception attacks and unmeasurable disturbances. Moreover, to resolve the unknown gains resulting from deception attacks, the adaptive attack compensator is proposed. Furthermore, in view of the logarithm Lyapunov function in the final step of the design process and the intelligent approximation technique, a new composite-observer-based adaptive consensus tracking control strategy is constructed. The suggested control strategy ensures the boundedness of all the closed-loop signals while also achieving synchronous tracking of the leader’s output by the followers. Last but not least, the effectiveness of the suggested control strategy is validated through two simulation examples.
{"title":"Composite-Observer-Based Adaptive Consensus Tracking Control for Nonlinear MASs With Unknown Control Directions Against Deception Attacks","authors":"Luyao Wen;Ben Niu;Xudong Zhao;Guangdeng Zong;Ding Wang;Wencheng Wang;Yuqiang Jiang","doi":"10.1109/TCYB.2024.3486934","DOIUrl":"10.1109/TCYB.2024.3486934","url":null,"abstract":"This article primarily studies the adaptive output-feedback consensus tracking control issue for nonlinear multiagent systems (MASs) with unknown control directions against deception attacks. First, a composite observer combining the state observer and the disturbance observer is developed to concurrently estimate the states of confronting deception attacks and unmeasurable disturbances. Moreover, to resolve the unknown gains resulting from deception attacks, the adaptive attack compensator is proposed. Furthermore, in view of the logarithm Lyapunov function in the final step of the design process and the intelligent approximation technique, a new composite-observer-based adaptive consensus tracking control strategy is constructed. The suggested control strategy ensures the boundedness of all the closed-loop signals while also achieving synchronous tracking of the leader’s output by the followers. Last but not least, the effectiveness of the suggested control strategy is validated through two simulation examples.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 1","pages":"343-354"},"PeriodicalIF":9.4,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142599355","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 : 2024-11-08DOI: 10.1109/TCYB.2024.3485889
Xin Jin;Yang Tang;Yang Shi;Xiaotai Wu;Wei Lin
The event-triggered almost global attitude consensus problem is considered in this article for multiple rigid body systems with prescribed performance. Two kinds of attitude consensus protocols using axis-angle vectors are proposed at the kinematic level with different prescribed performance constraints. The first protocol aims to achieve the event-triggered attitude consensus almost globally under jointly connected graphs. Based on a prescribed performance function with local states, the configuration space of parameterized attitude representations is shown to be positively invariant which almost globally covers $mathbb {SO}(3)$ . The second protocol is designed to reach attitude consensus with the prescribed transient behavior guaranteed in the event-triggered setting. By defining a prescribed performance function using the metric on axis-angle spaces, a dynamic event-triggered framework is designed to ensure both the attitude geometric topology constraint and prescribed convergence performance. Finally, numerical results are given to show the validness of the two control protocols.
{"title":"Event-Triggered Attitude Consensus of Multiple Rigid Body Systems With Prescribed Performance","authors":"Xin Jin;Yang Tang;Yang Shi;Xiaotai Wu;Wei Lin","doi":"10.1109/TCYB.2024.3485889","DOIUrl":"10.1109/TCYB.2024.3485889","url":null,"abstract":"The event-triggered almost global attitude consensus problem is considered in this article for multiple rigid body systems with prescribed performance. Two kinds of attitude consensus protocols using axis-angle vectors are proposed at the kinematic level with different prescribed performance constraints. The first protocol aims to achieve the event-triggered attitude consensus almost globally under jointly connected graphs. Based on a prescribed performance function with local states, the configuration space of parameterized attitude representations is shown to be positively invariant which almost globally covers \u0000<inline-formula> <tex-math>$mathbb {SO}(3)$ </tex-math></inline-formula>\u0000. The second protocol is designed to reach attitude consensus with the prescribed transient behavior guaranteed in the event-triggered setting. By defining a prescribed performance function using the metric on axis-angle spaces, a dynamic event-triggered framework is designed to ensure both the attitude geometric topology constraint and prescribed convergence performance. Finally, numerical results are given to show the validness of the two control protocols.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 1","pages":"307-320"},"PeriodicalIF":9.4,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142597394","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}
In this article, an adaptive prescribed-time neural controller is developed for the tracking problem of a class of high-order nonlinear systems with full-state constraints. First, a prescribed-time bounded stability criterion is designed. Then, to handle the “explosion of complexity” problem of the backstepping method, an adaptive prescribed-time filter is constructed, in which the filter error is prescribed-time stable. Compared with existing methods, the newly designed transformation approach can accommodate a broader range of state constraint types. Then, the unknown nonlinear function is handled by radial basis function neural networks (RBFNNs). The adaptive prescribed-time neural control scheme is developed based on above. It can guarantee that the closed-loop system achieves the prescribed-time stability, and all states do not transgress the constraints. To demonstrate the effectiveness of the control strategy, comparative simulations are provided at the end.
{"title":"Adaptive Prescribed-Time Filtered Control Design for a Full-State Constrained Nonlinear System","authors":"Fang Wang;Zikai Gao;Xiaoxian Xie;Chao Zhou;Changchun Hua","doi":"10.1109/TCYB.2024.3486721","DOIUrl":"10.1109/TCYB.2024.3486721","url":null,"abstract":"In this article, an adaptive prescribed-time neural controller is developed for the tracking problem of a class of high-order nonlinear systems with full-state constraints. First, a prescribed-time bounded stability criterion is designed. Then, to handle the “explosion of complexity” problem of the backstepping method, an adaptive prescribed-time filter is constructed, in which the filter error is prescribed-time stable. Compared with existing methods, the newly designed transformation approach can accommodate a broader range of state constraint types. Then, the unknown nonlinear function is handled by radial basis function neural networks (RBFNNs). The adaptive prescribed-time neural control scheme is developed based on above. It can guarantee that the closed-loop system achieves the prescribed-time stability, and all states do not transgress the constraints. To demonstrate the effectiveness of the control strategy, comparative simulations are provided at the end.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 1","pages":"321-331"},"PeriodicalIF":9.4,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142598218","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 : 2024-11-05DOI: 10.1109/TCYB.2024.3474714
Guanghao Lyu;Zhouhua Peng;Jun Wang
This article addresses the safety-critical receding-horizon planning and formation control of autonomous surface vehicles (ASVs) in the presence of model uncertainties, environmental disturbances, as well as stationary and moving obstacles. A three-level formation control architecture is proposed with a safety-critical formation trajectory generation module at its high level, a collision-free guidance module at its middle level, and an anti-disturbance control module at its low level. Specifically, a safety-critical formation trajectory generator is designed by leveraging collaborative neurodynamic optimization to plan safe formation trajectories to track a given trajectory and avoid stationary obstacles in a receding-horizon manner. Based on control barrier functions, a collision-free line-of-sight guidance law is developed to generate safe guidance commands to avoid collision with moving obstacles and other vehicles. An anti-disturbance control law is customized with a finite-time convergent observer for a vehicle to follow the guidance command signals. Simulation and hardware-in-the-loop experimental results are elaborated to validate the efficacy of the proposed method for the receding-horizon planning and formation control of ASVs.
本文探讨了在存在模型不确定性、环境干扰以及静止和移动障碍物的情况下，自主水面飞行器（ASV）的安全关键后退地平线规划和编队控制。本文提出了一个三层编队控制架构，高层为安全临界编队轨迹生成模块，中层为无碰撞制导模块，低层为抗扰动控制模块。具体来说，安全关键编队轨迹生成器是通过利用协作神经动力学优化来规划安全编队轨迹，以跟踪给定轨迹并以后退地平线方式避开静止障碍物。基于控制障碍函数，开发了无碰撞视线制导法则，以生成安全制导指令，避免与移动障碍物和其他车辆发生碰撞。利用有限时间收敛观测器为车辆定制了抗干扰控制法则，使其能够遵循制导指令信号。详细阐述了仿真和硬件在环实验结果，以验证所提方法在 ASV 的后退地平线规划和编队控制方面的有效性。
{"title":"Safety-Critical Receding-Horizon Planning and Formation Control of Autonomous Surface Vehicles via Collaborative Neurodynamic Optimization","authors":"Guanghao Lyu;Zhouhua Peng;Jun Wang","doi":"10.1109/TCYB.2024.3474714","DOIUrl":"10.1109/TCYB.2024.3474714","url":null,"abstract":"This article addresses the safety-critical receding-horizon planning and formation control of autonomous surface vehicles (ASVs) in the presence of model uncertainties, environmental disturbances, as well as stationary and moving obstacles. A three-level formation control architecture is proposed with a safety-critical formation trajectory generation module at its high level, a collision-free guidance module at its middle level, and an anti-disturbance control module at its low level. Specifically, a safety-critical formation trajectory generator is designed by leveraging collaborative neurodynamic optimization to plan safe formation trajectories to track a given trajectory and avoid stationary obstacles in a receding-horizon manner. Based on control barrier functions, a collision-free line-of-sight guidance law is developed to generate safe guidance commands to avoid collision with moving obstacles and other vehicles. An anti-disturbance control law is customized with a finite-time convergent observer for a vehicle to follow the guidance command signals. Simulation and hardware-in-the-loop experimental results are elaborated to validate the efficacy of the proposed method for the receding-horizon planning and formation control of ASVs.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"54 12","pages":"7236-7247"},"PeriodicalIF":9.4,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142582734","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 : 2024-11-05DOI: 10.1109/TCYB.2024.3485249
Ning Wang;Guang-Hong Yang
This study addresses a tampered-data recovery problem for linear discrete-time systems with completely unknown system dynamics under stealthy attacks. The basic idea is to identify the stealthy attack, that lies in any of attack-stealthy subspaces, and compensate for it. Different from the existing sparse recovery methods which are applicable to nonstealthy sparse attacks, a novel encoding scheme, where a set of subdecoding matrices is designed specifically for each 1-D attack-stealthy subspace, is developed so that the parameters of the stealthy attack can be identified via a subspace projection technique. A necessary and sufficient condition of determining the targeted subspace by using n parallel attack identification filters is established for this encoding scheme. Especially, a composite encoding matrix characterizes the lower and upper boundaries of the recovery error covariance’s trace. A simulation example of a flight vehicle illustrates the efficiency of the proposed approach.
本研究解决的是在隐蔽攻击下具有完全未知系统动态的线性离散时间系统的篡改数据恢复问题。其基本思想是识别位于任何攻击-隐蔽子空间中的隐蔽攻击，并对其进行补偿。与适用于非隐身稀疏攻击的现有稀疏恢复方法不同，我们开发了一种新颖的编码方案，即为每个一维攻击-隐身子空间专门设计一组子解码矩阵，从而可以通过子空间投影技术识别隐身攻击的参数。针对该编码方案，建立了使用 n 个并行攻击识别滤波器确定目标子空间的必要条件和充分条件。特别是，复合编码矩阵表征了恢复误差协方差迹线的下界和上界。飞行器的一个仿真实例说明了所提方法的效率。
{"title":"Data-Based Tampered-Data Recovery Strategy With Encoding Against Stealthy Attack for Unknown Discrete-Time Systems","authors":"Ning Wang;Guang-Hong Yang","doi":"10.1109/TCYB.2024.3485249","DOIUrl":"10.1109/TCYB.2024.3485249","url":null,"abstract":"This study addresses a tampered-data recovery problem for linear discrete-time systems with completely unknown system dynamics under stealthy attacks. The basic idea is to identify the stealthy attack, that lies in any of attack-stealthy subspaces, and compensate for it. Different from the existing sparse recovery methods which are applicable to nonstealthy sparse attacks, a novel encoding scheme, where a set of subdecoding matrices is designed specifically for each 1-D attack-stealthy subspace, is developed so that the parameters of the stealthy attack can be identified via a subspace projection technique. A necessary and sufficient condition of determining the targeted subspace by using n parallel attack identification filters is established for this encoding scheme. Especially, a composite encoding matrix characterizes the lower and upper boundaries of the recovery error covariance’s trace. A simulation example of a flight vehicle illustrates the efficiency of the proposed approach.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"54 12","pages":"7261-7271"},"PeriodicalIF":9.4,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142582732","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}
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