姜斌，南京航空航天大学校长，IEEE Fellow，教育部“长江学者”特聘教授。于东北大学自控系获博士学位，曾先后在新加坡、法国、美国、加拿大做博士后、研究员、特邀教授和访问教授。出版学术专著8部，发表国际期刊论文100余篇。他的研究领域包括智能故障诊断、容错控制及其在直升机、卫星和高铁上的应用，以第一完成人获国家自然科学二等奖。目前是亚太人工智能学会（AAIA）Fellow、中国自动化学会（CAA）会士，IEEE南京分部控制系统分会主席，以及IFAC技术过程故障检测、监督和安全性专业委员会成员。担任International Journal of Control, Automation and Systems资深编辑，以及IEEE Transactions on Cybernetics, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Industrial Informatics等国际期刊的副主编或编委会成员。

Bin Jiang, President of the Nanjing University of Aeronautics and Astronautics of China, IEEE Fellow, a Chair Professor of the Cheung Kong Scholar Program with the Ministry of Education of China. He received the Ph.D. degree in automatic control from Northeastern University, Shenyang, China. He had ever been a Post-Doctoral Fellow, a Research Fellow, an Invited Professor, and a Visiting Professor in Singapore, France, USA, and Canada, respectively. He has authored 8 books and over 100 referred international journal articles. His current research interests include intelligent fault diagnosis, fault tolerant control and their applications to helicopters, satellites, and high-speed trains. He was a recipient of the National Natural Science Award of China. He is also a Fellow of the Asia-Pacific Artificial Intelligence Association (AAIA), a Fellow of the Chinese Association of Automation (CAA), the Chair of Control Systems Chapter in IEEE Nanjing Section, and a member of IFAC Technical Committee on Fault Detection, Supervision, and Safety of Technical Processes. He currently serves as a Senior Editor for International Journal of Control, Automation and Systems, and an Associate Editor or an Editorial Board Member for several journals, such as the IEEE Transactions on Cybernetics, IEEE Transactions on Neural Networks and Learning Systems, and IEEE Transactions on Industrial Informatics.

题目：多智能体系统的故障诊断与容错控制及应用

摘要：近年来，随着多智能体系统复杂性的增加，在运行过程中包含单个智能体、通信网络等均极易发生故障，这影响着系统的稳定性和安全性。随着对系统可靠性要求的不断提高，故障诊断和容错控制研究引起了人们的高度关注。本次报告包含多智能体系统的故障诊断和容错控制。故障诊断部分提供了分布式非线性故障诊断设计方法，并考虑了基于事件触发机制的故障诊断策略。容错控制部分包括基于快速自适应技术、预设性能等设计方法。研究对象包括同构多智能体系统和异构多智能体系统。最后，利用实际无人编队系统对设计的协同容错控制方法进行了验证。

Title：Fault diagnosis and fault-tolerant control of multiagent systems and their applications

Abstract: In recent years, with the increasing complexity of multiagent systems, they are prone to faults during operation, including single agent faults and communication faults, affecting the stability and safety of the system. The demand for system reliability is continuously increasing, and the studies on fault diagnosis and fault-tolerant control have attracted high attention. This talk includes fault diagnosis and fault-tolerant control of multiagent systems. Fault diagnosis section provides a distributed nonlinear fault diagnosis design method and considers a fault diagnosis strategy based on event triggering mechanism. The fault-tolerant control section includes theoretical results based on fast adaptation, prescribed performance, etc. The research subjects include homogeneous multiagent systems and heterogeneous multiagent systems. Finally, the cooperative fault-tolerant control of some actual unmanned formation systems is demonstrated.

Prof. Christoforos Hadjicostis, IEEE Fellow, received B.S. degrees, the M.Eng. degree, and the Ph.D. degree in Electrical Engineering and Computer Science, all from the Massachusetts Institute of Technology (MIT). From 1999 to 2007, he was Assistant and then Associate Professor with the Department of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign. Since 2007, he is with the Department of Electrical and Computer Engineering at the University of Cyprus, where he is currently Professor and Interim Director of the DAEDALUS Research Center on Cyber-Physical Systems. His research focuses on fault diagnosis and tolerance in distributed dynamic systems; error control coding; monitoring, diagnosis, and control of large-scale discrete event systems; and related applications in embedded systems, distributed robotics, and anomaly detection and network security. He is the author of a book on “Estimation and Inference in Discrete-Event Systems” and three research monographs. He currently serves as Editor-in-Chief of the Journal of Discrete Event Dynamic Systems and as Senior Editor of IEEE Transactions on Automatic Control. 

Title: Exploiting Local Invariance for Resiliency in Distributed Average Consensus

Abstract: Recent developments in digital systems and networking technologies have led to the emergence of complex systems that need to be managed/controlled over cyber infrastructures, such as wireless and wired broadband networks. The emergence of this type of network control systems, which range from smart grids and transportation networks of various sorts, to embedded electronic devices and robotic networks, has sparked huge interest in distributed control and coordination problems. In this talk, we present recent progress in this area by focusing on an operation that is key for several such tasks: the distributed computation of the average (more generally, a weighted linear combination) of distinct parameters held at different agents of a multi-agent system. We discuss challenges pertaining to adversarial network conditions, such as packet drops on the communication links and/or the presence of faulty/malicious agents. In particular, we report on key invariance properties that can be checked locally to distributively perform error detection/correction. The resulting approach allows non-faulty/non-malicious agents to collectively remove the effects of previous communication exchanges with agents that are subsequently determined to be faulty/malicious.

唐漾，博士，教授，博士生导师， IEEE Fellow/AAIA Fellow, 国家级高层次人才（2019年度）、科技部中青年科技创新领军人才（2019年度）、国家级高层次青年人才（2014年度）, 上海市优秀学术带头人（2020年度）和德国洪堡基金等计划入选者。主要研究智能无人系统，工业智能和智能系统等。围绕上述领域，在Nature子刊，Cell子刊，Physics Reports，Physical Review系列, CVPR，ICCV, IJCAI, NeurIPS, SIAM会刊，Automatica和IEEE汇刊等上发表论文200余篇（含Automatica、IEEE汇刊和CCF-A类论文150余篇），申请/公开/授权专利20余件。主持国家科技部重点研发计划项目和课题, 3项国家自然科学基金重点类项目等。担任IEEE TCASI资深领域编辑，IEEE TNNLS, IEEE TCYB, IEEE TII, IEEE/ASME TMECH, IEEE TCASI, IEEE TCDS, IEEE TETCI, IEEE SJ, EAAI (IFAC Journal)，中国科学：信息科学、自动化学报等多个国际期刊的副主编/编委，担任多本IEEE汇刊和Engineering客座主编/客座编辑/客座执行编辑，获得四次IEEE期刊最佳/杰出副编辑。获得2019年度上海市自然科学奖一等奖（第一完成人）。担任中国自动化学会大数据专委会副主任委员、中国自动化学会网联智能专业委员会副主任委员。指导的学生多人获得中国科协青年托举人才、中国自动化学会优博等。

Yang Tang (Fellow, IEEE) received the B.S. and Ph.D. degrees in electrical engineering from Donghua University, Shanghai, China, in 2006 and 2010, respectively. From 2008 to 2010, he was a Research Associate with The Hong Kong Polytechnic University, Hong Kong. From 2011 to 2015, he was a Post-Doctoral Researcher with the Humboldt University of Berlin, Berlin, Germany, and with the Potsdam Institute for Climate Impact Research, Potsdam, Germany. He is now a Professor with the East China University of Science and Technology, Shanghai. His current research interests include distributed estimation/control/optimization, computer vision, reinforcement learning, cyber-physical systems, hybrid dynamical systems, and their applications. Prof. Tang is an IEEE Fellow. He was a recipient of the Alexander von Humboldt Fellowship. He is an Senior Area Editor of IEEE Transactions on Circuits and Systems-I: Regular Papers, Associate Editor of IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Cybernetics, IEEE Transactions on Industrial Informatics, IEEE/ASME Transactions on Mechatronics, IEEE Transactions on Circuits and Systems-I: Regular Papers, IEEE Transactions on Cognitive and Developmental Systems, IEEE Transactions on Emerging Topics in Computational Intelligence, IEEE Systems Journal, Engineering Applications of Artificial Intelligence (IFAC Journal), Science China Information Sciences and Acta Automatica Sinica, etc. He has published more than 200 papers in international journals and conferences, including more than 130 papers in IEEE Transactions and 20 papers in IFAC journals. He has been awarded as best/outstanding Associate Editor in IEEE journals for four times. He is a (leading) guest editor for several special issues focusing on autonomous systems, robotics, and industrial intelligence in IEEE Transactions.

题目：低成本场景智能感知与理解

摘要：智能无人系统的感知与理解是当前人工智能领域的研究热点，多年来受到各界的广泛关注。本报告将分别介绍团队在感知任务精准性和迁移性进行介绍。最后，本报告对智能无人系统中的感知与理解的未来研究方向进行了总结与展望。

Title: Intelligent Perception and Understanding with Low Cost

Abstract: The perception and understanding of intelligent unmanned systems are current research hotspots in the field of artificial intelligence and have received extensive attention. This report will introduce the team's accuracy and transferability of perception tasks. Finally, this report summarizes and looks forward to the future research directions of perception and understanding in intelligent unmanned systems.

Prof. Hyo-Sung Ahn,  Fellow of ICROS and Senior Member of IEEE, received the B.S. and M.S. degrees in astronomy from Yonsei University, Seoul, South Korea, in 1998 and 2000, respectively, the M.S. degree in electrical engineering from the University of North Dakota, Grand Forks, ND, USA, in 2003, and the Ph.D. degree in electrical engineering from Utah State University, Logan, UT, USA, in 2006. Since July 2007, he has been with the School of Mechatronics and the School of Mechanical Engineering, Gwangju Institute of Science and Technology (GIST), Gwangju, South Korea. He was a Dasan Distinguished Professor (Dasan Professor), from 2013 to 2018. Before joining GIST, he was a Senior Researcher with the Electronics and Telecommunications Research Institute, Daejeon, South Korea. He was a Visiting Scholar with the Colorado School of Mines in 2019. He is currently a Professor with the School of Mechanical Engineering, GIST. Dr. Ahn is a Fellow of ICROS and Senior Member of IEEE, and he is serving as an Editor-in-Chief of International Journal of Control, Automation, and Systems. He is the author of the books Iterative Learning Control: Robustness and Monotonic Convergence for Interval Systems (Springer, 2007), Formation Control: Approaches for Distributed Agents (Springer, 2020), and a co-author of the book Control of Multi-agent Systems: Theory and Simulation with Python (Springer, 2024). He is the recipient of the Presidential Commendation of Korea (2024). His research interests include distributed control, aerospace navigation and control, network localization, and learning control.

Title: Distributed formation control: Motivations, theory and implementations

Abstract: This talk firstly presents the motivations of distributed formation control problems. Inspired from collective behaviors of animals, the distributed formation control laws use relative information defined in local coordinate frames. Thus, from sensing and actuation perspectives, the formation control laws are fully distributed. The graph theory is employed to describe the relative interactions among agents. Specifically, to define a unique configuration of formation, the rigidity theory is used. Consequently, as the second part of the talk, the concepts of framework, equivalence, congruence, and local and global rigidity are introduced. Based on the collective behavior mechanism of biological systems and the concepts of graph rigidity, distributed formation control laws are mathematically refined to show how they are related to the animal’s sensing mechanisms. Lastly, the techniques for implementing the formation control laws to mobile multi-agent systems are introduced and experiment results are briefly presented. In this talk, we would like to seek for a further opportunity for applying the formation control algorithms to network systems including network localization, and analysis of social and complex networks.

潘成胜，南京信息工程大学特聘教授、博导，国家百千万人才工程百层次人才。现兼任军委科技委X领域专家委员会委员、航天总体与卫星载荷技术专家组专家，兼任装备发展部X网络专业组专家、X通信专业组专家、X智能专业组专家，教育部计算机教学指导委员会委员等职。主要研究方向为智能网络理论与关键技术，在网络流量理论、异构链路汇聚和网络统一承载等方面取得了系统性创新成果，完成了系列国家重大工程。获国家科技进步二等奖2项，国防科技进步一等奖3项，省技术发明/科技进步一等奖3项。1998年获得国务院特殊津贴，2007年入选“新世纪百千万人才工程”国家级人选，2016年获得全国五一劳动奖章，2019入选中国科学院院士有效候选人。

Prof. Chengsheng Pan is a doctoral supervisor at Nanjing University of Information Science and Technology and a distinguished talent in the National Hundred, Thousand, and Ten Thousand Talents Project. He currently serves as a member of the Expert Committee in the X field of the Central Military Commission's Science and Technology Commission, a specialist in the Aerospace and Satellite Payload Technology Expert Group, and an expert in the X Network, X Communications, and X Intelligence Expert Groups under the Equipment Development Department. He is also a member of the Computer Science Teaching Steering Committee under the Ministry of Education. His primary research focuses on intelligent network theory and key technologies, with notable contributions in network traffic theory, heterogeneous link convergence, and unified network carrying. Professor Pan has achieved systematic and innovative breakthroughs in these areas, completing a series of major national projects. He has been awarded two National Science and Technology Progress Awards (Second Class), three National Defense Science and Technology Progress Awards (First Class), and three Provincial Technology Invention/Science and Technology Progress Awards (First Class). In 1998, he received a special government allowance from the State Council, and in 2007, he was selected as a national-level candidate for the "New Century Hundred, Thousand, and Ten Thousand Talents Project." In 2016, he was awarded the National May Day Labor Medal, and in 2019, he was nominated as a valid candidate for Academician of the Chinese Academy of Sciences.

题目：有/无人协同边缘网络智能组网技术

摘要：针对强对抗、高机动复杂环境下，有/无人协同边缘网络智能组网面临的新挑战，以“构建网络自配置高效互联、链路自优化敏捷互联、节点自修复韧性互联的有/无人边缘协同网络”为目标，介绍了有/无人边缘协同网络的发展与现状；针对网络初始配置导致网络覆盖率低下的难题，阐述了有/无人边缘协同网络高效互联技术；针对链路自优化受限导致网络难以敏捷互联的难题，阐述了有/无人边缘协同网络敏捷互联技术；针对节点自修复能力不足导致网络难以韧性互联的难题，阐述了有/无人边缘协同网络韧性互联技术。

Title: Intelligent Networking Technologies for Manned/Unmanned Collaborative Edge Networks

Abstract: In response to the new challenges faced by manned/unmanned collaborative edge networks operating in highly contested and high-mobility complex environments, this report aims to establish a framework for constructing such networks with self-configuring and efficient interconnectivity, self-optimizing and agile link connections, and self-healing and resilient node interactions. The development and current state of manned/unmanned collaborative edge networks are comprehensively reviewed. To address the issue of low network coverage resulting from initial network configuration, advanced techniques for achieving efficient interconnectivity are discussed. Furthermore, solutions for enabling agile interconnectivity are presented to overcome limitations in link self-optimization. Lastly, methodologies for enhancing network resilience through improved node self-healing capabilities are elaborated upon, providing robust strategies for maintaining consistent and reliable connectivity in challenging operational scenarios.

张海涛，华中科技大学人工智能与自动化学院副院长、二级教授、华中科技大学首席教授、博导、国家杰青、国家优青、青年拔尖、青年长江。华中学者领军岗教授、自主智能无人系统教育部工程研究中心主任、广东省全自主无人艇工程技术研究中心主任、国家科技创新2030新一代人工智能重大项目首席科学家。2005年在中科大获得博士学位，2007年在剑桥从事博士后研究， 2010年晋升为教授，博导。从事群体智能、无人艇集群协同等领域研究。发表SCI论文150余篇，含Nature Machine Intelligence、Nature Communications、National Science Review、Automatica和IEEE汇刊100余篇。理论成果被Nature Physics研究亮点报道，应用成果在中船重工、广船国际等转化，支撑大湾区跨海通道、海上风电场等国家重大海洋工程建设和南海油气资源勘测。排名第一获湖北省自然科学一等奖2项和广东省技术发明一等奖1项。入选斯坦福全球前2%顶尖科学家榜单。担任/曾任IEEE Trans. SMC-Systems, IEEE Trans. CASII，Engineering等国际期刊编委。

Prof. Zhang Hai-Tao is the Deputy Dean of the School of Artificial Intelligence and Automation at Huazhong University of Science and Technology (HUST). He is a chief professor of HUST, doctoral supervisor, and a recipient of prestigious honors including the NSFC Outstanding Youth Fund and NSFC Excellent Youth Fund. Prof. Zhang’s research interests include of marine unmanned systems and swarm intelligence. Prof. Zhang serves as the Director of the Engineering Research Center for Autonomous Intelligent Unmanned Systems of Chinese Ministry of Education and the Engineering Technology Research Center for Fully Autonomous Surface Vehicles (ASVs) of Guangdong Province. He is also the Chief Scientist of the National Science and Technology Innovation 2030 Major Project for New Generation Artificial Intelligence. Prof. Zhang received his Ph.D. from the University of Science and Technology of China (USTC) in 2005, followed by postdoctoral research at the University of Cambridge in 2007. He became a professor and doctoral supervisor in 2010. His research focuses on swarm intelligence and multi-ASV fleet collaboration. He has published over 150 SCI-indexed papers, with more than 100 papers in high-impact journals such as Nature Machine Intelligence, Nature Communications, National Science Review, Automatica, and IEEE Transactions. His theoretical work has been highlighted in Nature Physics. Some invention patents have been commercialized in China Shipbuilding Industry Corporation and Guangzhou Shipyard International LTD. He has been awarded two first prizes in Hubei Natural Science and one first prize in Guangdong Technology Invention, and was recognized among the top 2% of scientists worldwide by Stanford University. Prof. Zhang also serves on the editorial boards of renowned international journals, including IEEE Trans. SMC-Systems, IEEE Trans. CASII, Engineering and Unmanned Systems.

题目：额外连边对网络化无人艇集群协同性能的调控机理研究

摘要：网络化无人系统集群的拓扑结构中的反向连边结构对整个系统同步性能影响深远。本工作建立了拓扑扰动的网络拉普拉斯谱域分析框架，定量揭示了有向图网络主收敛速率的受扰动情况，阐明了反边对网络同步信息流的作用机理。据此，在额外连边机理建模、有向无环图反边影响同步充分必要条件、一般性网络拓扑结构额外作用规律等关键科学问题上取得了理论进展，并应用于异构无人艇集群协同探测、拒止、围捕和对抗博弈。

Title: Regulating Mechanism of Additional Edges on the Synchronization Performance of Networked  Autonomous Surface Vehicle Fleets

Abstract: This study establishes a comprehensive framework for analyzing the influence of reverse edge structures within the topology of networked unmanned system groups, specifically focusing on their impact on synchronization performance. By proposing a method to evaluate the Laplacian spectrum of networks under topological perturbations, the study quantitatively reveals the effect of reverse edge structures on the dominant convergence rate in directed networks. The study further clarifies the mechanism by which these reverse edges could be used to regulate the information flow throughout the network. Based on this framework, the paper advances theoretical insights into critical scientific challenges, including the modeling of edge perturbation mechanisms, the necessary and sufficient conditions for reverse edges to affect synchronization in directed acyclic graphs, and the principles governing how additional topological structures influence network behavior. These theoretical advancements have practical applications, as they have been implemented in tasks involving the cooperative detection, denial, encirclement, and adversarial game strategies of heterogeneous autonomous surface vehicles (ASV) fleets.

温广辉，二级教授，东南大学首席教授，博士生导师（系统科学，网络空间安全），IET Fellow，国家杰出青年科学基金获得者，教育部国家级青年人才入选者，国家优秀青年科学基金获得者，某型号分系统副主任设计师，江苏国家应用数学中心副主任，江苏省信息数学应用中心常务副主任（主持工作）。2012年获北京大学力学系统与控制专业博士学位，长期从事自主智能无人系统、复杂网络系统、分布式控制与优化、弹性控制和分布式强化学习等领域的研究工作。在Nature Reviews Electrical Engineering、Research、The Innovation和IEEE汇刊发表学术论文200余篇，出版学术专著3部，SCI他人引用过万次，获国际学术期刊最佳论文奖1次（The 7th Kimura Best Paper Award of Asian Journal of Control）、国内外学术会议最佳论文奖4次。担任国际期刊IEEE/ASME Trans. Mechatronics, IEEE Trans. Neural Networks and Learning Systems, IEEE Trans. Industrial Informatics, IEEE J. Emerging and Selected Topics in Industrial Electronics, IEEE Trans. Systems, Man, and Cybernetics: Systems 和Asian J. Control编委，获Asian J. Control首届杰出编委奖。担任国内学术期刊《自动化学报》、《指挥与控制学报》和《系统工程与电子技术》编委。任中国指挥与控制学会副秘书长、中国自动化学会大数据专委会副主任、中国指挥与控制学会青年工作委员会副主任；主持国家杰青项目、优青项目，国家自然基金联合重点项目、科技部重点研发计划项目课题，中船重工集团横向课题等30余项科研项目；申请国家发明专利60余项，授权24项；授权国际PCT专利1项（美国发明专利）。获国家一级学会科学技术奖一等奖一项（排名第1），ARC DECRA Fellow(澳大利亚研究理事会早期职业研究者奖)，亚太神经网络学会青年杰出研究奖，中国指挥与控制学会青年科学家奖、创新奖一等奖等学术荣誉。2018年至今持续入选科睿唯安全球高被引学者榜单（工程领域）。

Prof. Guanghui Wen received his Ph.D. degree in Mechanical Systems and Control from Peking University, Beijing, China, in 2012. He is currently an Endowed Chair Professor in the Department of Systems Science at Southeast University, Nanjing, China. His current research interests include autonomous intelligent unmanned systems, complex networked systems, distributed control and optimization, resilient control, and distributed reinforcement learning. Professor Wen received the National Science Fund for Distinguished Young Scholars in 2023 and the Australian Research Council Discovery Early Career Researcher Award in 2017. He has published over 200 academic papers in journals such as Nature Reviews Electrical Engineering, Research, The Innovation, and various IEEE Transactions. His work has garnered significant attention, receiving over 10,000 non-self-citations according to Web of Science. He has been awarded The 7th Kimura Best Paper Award from the Asian Journal of Control and has received Best Paper Awards from both domestic and international academic conferences four times. He serves on the editorial boards of several international journals, including IEEE/ASME Transactions on Mechatronics, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Industrial Informatics, IEEE Journal of Emerging and Selected Topics in Industrial Electronics, IEEE Transactions on Systems, Man, and Cybernetics: Systems, and the Asian Journal of Control, where he received the Outstanding Associate Editor Award in 2021. He also serves on the editorial boards of academic journals such as Acta Automatica Sinica (自动化学报), Journal of Command and Control (指挥与控制学报), and Systems Engineering and Electronics (系统工程与电子技术). Professor Wen has applied for over 60 national invention patents, with 24 granted, and holds one authorized international PCT patent (U.S. invention patent). He has received numerous academic honors, including a First-Class Science and Technology Award from a national first-class society (ranked first), ARC DECRA Fellow, Asia-Pacific Neural Network Society Young Distinguished Research Award, Chinese Association for Command and Control Young Scientist Award, and First Prize for Innovation Award. Since 2018, Professor Wen has been continuously listed as a Highly Cited Researcher by Clarivate Analytics in the field of engineering.

题目：分布式弹性协同控制与决策：理论进展与实际应用

摘要：近年来，随着感知、通信和控制技术的迅猛发展，多智能体系统分布式协同控制与决策技术展现出了广阔的应用前景。然而，由于多智能体系统的开放性和复杂性，这项技术在实际应用中常常面临信息-物理攻击（如恶意节点、DoS攻击等）的威胁，使得分布式协同控制与决策技术的实施面临严峻挑战。针对这一挑战，本报告系统地探讨了分布式弹性协同控制与决策的最新理论进展和实际应用。首先，介绍了一种分布式弹性协同控制的理论框架，分析了攻击对系统稳定性和协同任务性能的影响，提出了针对不同攻击类型（可补偿与不可补偿）的弹性控制策略，提升了系统的抗干扰能力和鲁棒性，并将相关理论结果应用于多无人车和无人艇集群的自主路径规划与安全编队控制中。其次，探讨了分布式弹性决策的理论与方法，提出了通信攻击下的快速分布式最优决策技术，进一步分析了节点动力学对最优决策的影响，并提出了基于具身智能的决策-控制一体化方法，展示了其在智能电源经济调度和无人艇集群协同感知与定位中的应用潜力。最后，展望了分布式弹性协同控制与决策的未来研究方向和发展趋势。

Title: Distributed Resilient Cooperative Control and Decision-Making: Theoretical Advances and Practical Applications

Abstract: In recent years, with the rapid development of sensing, communication, and control technologies, distributed cooperative control and decision-making techniques for multi-agent systems have shown great potential for application. However, due to the openness and complexity of multi-agent systems, these techniques often face significant threats from cyber-physical attacks (such as malicious nodes, DoS attacks, etc.) in practical applications, posing severe challenges to their implementation. To address these challenges, this report systematically explores the latest theoretical advancements and practical applications of distributed resilient cooperative control and decision-making. Firstly, it introduces a theoretical framework for distributed resilient cooperative control, analyzing the impact of attacks on system stability and cooperative task performance. It also proposes resilient control strategies tailored to different types of attacks (compensable and non-compensable), which enhance the system's resistance to interference and robustness. These theoretical results are then applied to autonomous path planning and secure formation control in swarms of unmanned vehicles and boats. Secondly, the report delves into the theory and methods of distributed resilient decision-making, presenting a fast distributed optimal decision-making technique under communication attacks. It further analyzes the influence of node dynamics on optimal decision-making and proposes an integrated decision-control approach based on embodied intelligence, demonstrating its potential applications in economic dispatch of intelligent power sources and cooperative sensing and positioning in unmanned boat swarms. Finally, the report looks forward to future research directions and development trends in resilient cooperative control and decision-making. 

忻欣，国家级高层次人才，东南大学首席教授，博士生导师，复杂工程系统测量与控制教育部重点实验室主任，东南大学智能无人系统研究院执行院长。1987年本科毕业于中国科学技术大学，同年免试推荐为东南大学硕士研究生，1991-1993年获得日本文部省奖学金，作为中日联合培养博士生在大阪大学进行研究，1993年获得东南大学工学博士学位。2000年又获得日本东京工业大学博士（工学）学位。1993-1995年在东南大学从事博士后研究，1995-1996年任东南大学副教授，1996-1997年任日本新能源产业技术综合开发机构（NEDO）最先端领域技术研究员，1997-2000年任东京工业大学助理教授，2000-2007年任冈山县立大学副教授，2008-2023年任冈山县立大学教授，曾任该校计算机和系统工程学院长助理、校国际交流中心副主任、系统工程系主任。在IEEE Transactions on Automatic Control、IEEE Transactions on Robotics、Automatica等学术期刊和重要国际会议上发表论文240余篇，出版专著6部。1988年获第二届全国机器人学术会议优秀论文奖，2004年获日本计测与自动控制学会（SICE）控制部门大会奖。参与日本国家级重大科研项目3项，主持日本国家科学研究基金项目6项，日本三个研究财团的研究基金项目5项。主持过中国博士后基金项目、中国国家自然科学基金青年项目、中国国家自然科学基金面上项目、国家高层次人才项目和江苏省双创人才项目。曾任国家973专家组成员、日本计测与自动控制学会控制理论委员会委员，现任中国自动化学会控制理论专业委员会委员、IFAC的Large Scale Complex Systems的技术委员会委员、IEEE高级会员等。曾担任过IEEE Control Systems Letters、日本计测与自动控制学会论文杂志、日本机器人学会论文杂志的编委以及50多个国际学术会议的程序委员会委员。现为Automatica编委。研究领域包括机器人系统的智能与非线性控制理论及实验验证、智能仿生机器人、人形机器人等。

Xin Xin received his B.S. degree in 1987 from the University of Science and Technology of China, Hefei, China, and his Ph.D. in 1993 from the Southeast University, Nanjing, China. From 1991 to 1993, he conducted his Ph.D. studies in Osaka University as a co-advised student of China and Japan under the Japanese Government Scholarship. He also received a Doctor degree in engineering in 2000 from Tokyo Institute of Technology. From 1993 to 1995, he was a postdoctoral researcher and then became an associate professor of Southeast University. From 1996 to 1997, he was with the New Energy and Industrial Technology Development, Japan as an advanced industrial technology researcher. From 1997 to 2000, he was an assistant professor of Tokyo Institute of Technology. Starting in 2000, he joined Okayama Prefectural University as an associate professor and was promoted to professor in 2008, a position he held until 2023. He has held roles such as assistant dean of the School of Computer Science and Systems Engineering, the deputy director of the International Exchange Center, and the head of the Department of Systems Engineering at Okayama Prefectural University. He is currently a chief professor of Southeast University and the director of the Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Southeast University. He is also the deputy dean of the Institute of Intelligent Unmanned Systems at Southeast University. He served on the associate editor of journals for the Robotics Society of Japan, the Society of Instrument and Control Engineers (SICE) of Japan, and the IEEE Control Systems Letters. He is currently an associate editor for Automatica. He is now a member of technical committee of large scale complex systems of IFAC. He has over 240 publications in journals, international conferences, monographs and book chapters. He received the excellent paper award of the second national conference on robotics in 1988, and the conference paper award of SICE 3rd Annual Conference on Control Systems in 2004. He participated in three national-level major scientific research projects in Japan, led six projects funded by the Japan Society for the Promotion of Science (JSPS), and five projects funded by three Japanese research foundations. He also led projects funded by the China Postdoctoral Science Foundation, the National Natural Science Foundation of China's (NSFC) youth fund and general program, national high-level talent projects, and provincial dual-innovation talent projects. His current research interests include humanoid robotics, intelligent systems, dynamics and control of nonlinear and complex systems.

题目：欠驱动机器人系统控制设计与分析的新见解与进展

摘要：欠驱动是指系统中的执行器数量少于自由度的特性，这一特性不仅存在于机械系统中，如人形机器人、特技机器人和软体机器人，还出现在许多生物系统中。这些欠驱动系统既给机器人与控制领域带来了独特的挑战，也提供了重要的机遇，促进了机器人与控制之间的深入互动，收到这两个领域研究者的广泛关注。本报告首先展示了欠驱动系统在生物系统和机电系统中的众多实例。其次，提供了在过去二十年中深入研究的多种欠驱动机器人系统的控制设计与分析的新见解与进展。这些系统包括欠驱动度为二的刚性多自由度系统，以及软体机器人。通过引入新概念、揭示关键特征和提出先进的设计方法，重点研究了摆起控制、强结构可控性、强结构可观测性和强稳定性等挑战性课题。这些创新对于理解和改进欠驱动机器人系统的控制与稳定性至关重要。我们还通过对多种系统的实体实验，验证了我们的理论成果。最后，探讨了未来在欠驱动机器人系统设计与分析方面的有前景的研究方向，旨在进一步推动该领域的发展。

Title: New Insights and Advances in Control Design and Analysis for Underactuated Robotic Systems

Abstract: Underactuation refers to systems with fewer actuators than degrees of freedom, a characteristic found not only in mechanical systems such as humanoid robots, acrobatic robots, and soft robots, but also in many biological systems. These underactuated systems present unique challenges and opportunities, fostering rich interactions between robotics and control, and have drawn significant attention from researchers in these fields. In this talk, we present numerous examples of underactuated systems across various domains, including biological systems. We provide new insights and advances in the control design and analysis of a wide range of underactuated robotic systems that we have extensively studied over the past two decades. This includes rigid multi-degree-of-freedom systems with an underactuation degree of two, as well as soft robots. We introduce novel concepts, key features, and advanced design techniques, with particular focus on swing-up control, strong structural controllability, strong structural observability, and strong stabilizability. These innovations are critical for understanding and improving the control and stability of underactuated robotic systems. We validate our theoretical findings through experimental results on various systems. Finally, we explore promising future research directions to further push the boundaries of underactuated robotic system design and analysis.

洪华杰研究员，国防科技大学机械电子工程方向学科带头人，博士生导师，长期从事光机电一体优化设计、目标跟踪控制、可调谐视觉成像组件、无人作战系统研究工作，173首席科学家，主持和参加国家自然科学基金、973/173重点、科技委创新特区、装备预研等项目30余项，获军事科技进步一等奖1项、湖南省技术发明一等奖1项、国防技术方面三等奖1项，发表学术论文80余篇，申请国家发明专利20余项。

Prof. Huajie Hong, Leader of the Mechanical and Electronic Engineering discipline at the National University of Defense Technology, doctoral supervisor, has been engaged in research on the optimization design of optomechatronic integration, target tracking control, tunable visual imaging components, and unmanned combat systems. As the chief scientist of Project 173, he has led and participated in more than 30 projects including the National Natural Science Foundation, 973/173 key projects, Science and Technology Commission innovation zones, and equipment pre-research. He has won the first prize of Military Science and Technology Progress, the first prize of Hunan Province Technology Invention, and the third prize of National Defense Technology. He has published more than 80 academic papers and applied for more than 20 national invention patents.

题目：无人系统视觉感知的光学超表面技术原理

摘要：光学载荷在微小型无人系统中应用广泛，光学超表面因其在亚波长尺度上实现对光波的精确调控，近年来在智能视觉感知领域展现出巨大的潜力。本报告从超表面的概念入手，深入探讨其光学调控原理和关键机制。结合视觉感知应用，详细介绍了超表面在相位调控（如消像差成像、变焦距成像、相位编码成像）、光谱调控（如消色差成像、高光谱成像）、偏振调控（如偏振复用、偏振路由）、紧凑深度/广度成像及复合调制感知中的应用，以及探索基于超表面的计算成像，突破传统成像元件的局限，提升成像系统的多维感知能力。在总结超表面技术在实际应用中的挑战的同时，展望其在未来智能感知领域的发展前景与潜在创新方向。

Title: Optical Metasurface Technology Principles for Visual Perception in Unmanned Systems

Abstract: Optical payloads are widely used in small and micro unmanned systems. Optical metasurfaces, due to their precise control of light waves at the sub-wavelength scale, have shown great potential in the field of intelligent visual perception in recent years. This report starts with the concept of metasurfaces and delves into their optical control principles and key mechanisms. Combined with visual perception applications, it introduces in detail the application of metasurfaces in phase control (such as aberration-free imaging, zoom imaging, phase-coded imaging), spectral control (such as achromatic imaging, hyperspectral imaging), polarization control (such as polarization multiplexing, polarization routing), compact depth/wide-angle imaging, and composite modulation perception. It also explores computational imaging based on metasurfaces, breaking through the limitations of traditional imaging components, and enhancing the multi-dimensional perception capabilities of imaging systems. While summarizing the challenges of metasurface technology in practical applications, it also looks forward to its development prospects and potential innovative directions in the future field of intelligent perception.

葛泉波，南京信息工程大学教授，博士生导师，自动化学院院长，江苏省大气环境与装备技术协同创新中心主任，江苏省智能气象探测机器人工程研究中心主任。江苏省高校“青蓝工程”中青年学术带头人，获中国自动化学会第四届青年科学奖，美国明尼苏达大学电子与计算机工程系访问学者。发表和录用包括 IEEE TAC、IEEE TIE、IEEE TC、IEEE TSP、自动化学报等高质量学术论文近百篇，主持国家自然科学基金面上和重点项目等三十余项科研项目。是中国自动化学会青年工作委员会副主任委员，中国人工智能学会自主无人系统专业委员会副主任委员，江苏省自动化学会自主无人系统专委会常务副主任等。是 IEEE TSMC:Systems和 International Journal of Systems Science 的 Associate Editor，《自动化学报》、《指挥与控制学报》和《控制工程》期刊编委。主要研究领域包括自主智能无人系统、智能感知与学习控制、动力系统智能化等。

Quanbo Ge, is a professor at Nanjing University of Information Science and Technology, a doctoral advisor, and the dean of the School of Automation. He serves as the director of the Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET) and the Jiangsu Provincial Engineering Research Center for Intelligent Meteorological Detection Robotics. He is a key academic leader in the "Qinglan Project" for young and middle-aged scholars in Jiangsu Province, a recipient of the Fourth Youth Science Award from the Chinese Association of Automation, and a visiting scholar in the Department of Electrical and Computer Engineering at the University of Minnesota. He has published nearly 100 high-quality academic papers in journals such as IEEE TAC, IEEE TIE, IEEE TC, IEEE TSP, and the Journal of Automation, and has led over 30 research projects, including general and key projects funded by the National Natural Science Foundation of China. He serves as the deputy chairman of the Youth Work Committee of the Chinese Association of Automation, the vice-chairman of the Autonomous Unmanned Systems Professional Committee of the Chinese Artificial Intelligence Society, and the executive deputy chairman of the Autonomous Unmanned Systems Committee of the Jiangsu Province Automation Society. He is also an associate editor for IEEE TSMC: Systems and the International Journal of Systems Science, and a member of the editorial boards of the Journal of Automation, the Journal of Command and Control, and Control Engineering. His main research areas include autonomous intelligent unmanned systems, intelligent perception and learning control, and the intelligence of dynamic systems.

题目：无人机/船协同系统的可信位姿估计理论与验证

摘要：无人机和无人船的协同在众多民用和国防领域有着重要且广泛的应用。无人机/船协同系统高性能安全工作的核心保障是无人机具备自主降落到无人船上的能力，而这个能力实现的基础是协同系统拥有强大精准的无人机/船相对位姿测量能力。由于传统模型驱动的Kalman滤波理论无法在复杂环境下为协同系统提供一个可靠的位姿估计输出，其关键是滤波模型与实际协同系统运动模型之间存在无法克服的失配性，从而导致传统状态估计理论的性能度量机制被破坏，输出结果的可用性受到质疑。姿态估计可信度评价问题的提出能够很好的解决上述模型失配而导致的滤波输出结果应用的不可靠问题，从而建立了一种全新的、可信度理论驱动的新型自适应估计器设计方法。本报告以可信度Kalman滤波理论为核心逻辑框架，以协同位姿观测系统的非高斯干扰识别为出发点，开展非线性非高斯可信容积Kalman滤波融合方法研究以及在无人机/船协同降落中的实验验证研究，应用高斯和框架、最大相关熵滤波、特征函数滤波、混合高斯核理论、协方差匹配、宽度学习系统等的深入联合来构建一套面向协同系统高性能位姿估计算法体系，可为无人机/船协同系统的高效稳定安全的运行提供快速、精准和稳定的位姿估计输出。

Title: Theory and Verification of Trusted Pose Estimation for Unmanned Aircraft/Ship Collaborative Systems

Abstract: The collaboration between drones and unmanned ships has significant and widespread applications in various civil and defense fields. The core guarantee for the high-performance and safe operation of UAV/ship collaborative systems is the drone's ability to autonomously land on the unmanned ship. This capability relies on the strong and precise relative pose measurement capability of the collaborative system between the drone and the ship. Traditional model-driven Kalman filtering theory fails to provide reliable pose estimation outputs for collaborative systems in complex environments. The key issue is the irreconcilable mismatch between the filtering model and the actual motion model of the collaborative system, which undermines the performance measurement mechanism of conventional state estimation theories, thereby raising doubts about the usability of the output results. The proposal of a reliability assessment for pose estimation effectively addresses the reliability issues in filtering outputs caused by model mismatches. This leads to the establishment of a novel design approach for adaptive estimators driven by credibility theory. This report focuses on the credibility Kalman filtering theory as the core logical framework, starting from the identification of non-Gaussian disturbances in collaborative pose observation systems. It explores nonlinear non-Gaussian credible volume Kalman filtering fusion methods and experimental validation in UAV/ship collaborative landing scenarios. By deeply integrating Gaussian frameworks, maximum entropy filtering, characteristic function filtering, mixed Gaussian kernel theory, covariance matching, and wide learning systems, a high-performance pose estimation algorithm system tailored for collaborative systems is constructed. This approach can provide rapid, precise, and stable pose estimation outputs for the efficient, stable, and safe operation of UAV/ship collaborative systems.