Prof. Alessandro Astolfi, Imperial College London, U.K., IEEE Fellow, Member of the Academy of Europe

Alessandro Astolfi (IEEE Fellow) graduated in electronic engineering from the University of Rome in 1991. In 1992 he joined ETH-Zurich where he obtained a M.Sc. in Information Theory in 1995 and the Ph.D. degree with Medal of Honor in 1995 with a thesis on discontinuous stabilization of nonholonomic systems. In 1996 he was awarded a Ph.D. from the University of Rome “La Sapienza” for his work on nonlinear robust control. Since 1996 he has been with the Electrical and Electronic Engineering Department of Imperial College London, London (UK), where he is currently Professor of Nonlinear Control Theory and College Consul for the Faculty of Engineering and Business School. From 2010 to 2022 he served as Head of the Control and Power Group at Imperial College London and from 1998 to 2003 he was an Associate Professor at the Dept. of Electronics and Information of the Politecnico of Milano. Since 2005 he has also been a Professor at Dipartimento di Ingegneria Civile e Ingegneria Informatica, University of Rome Tor Vergata. His research interests are focused on mathematical control theory and control applications, with special emphasis for the problems of discontinuous stabilization, robust and adaptive control, observer design and model reduction. He is the author of over 180 journal papers; 30 book chapters; and over 270 papers in refereed conference proceedings. He is the author (with D. Karagiannis and R. Ortega) of the monograph “Nonlinear and Adaptive Control with Applications” (Springer-Verlag). He is the recipient of the IEEE CSS A. Ruberti Young Researcher Prize (2007), the IEEE RAS Googol Best New Application Paper Award (2009), the IEEE CSS George S. Axelby Outstanding Paper Award (2012), the Automatica Best Paper Award (2017), and the IEEE Trans, on Control Systems Technology Best Paper Award (2023). He is a “Distinguished Member” of the IEEE CSS, IEEE Fellow, IFAC Fellow and Member of the Academia Europaea. He served as Associate Editor for Automatica, Systems and Control Letters, the IEEE Trans. on Automatic Control, the International Journal of Control, the European Journal of Control and the Journal of the Franklin Institute; as Area Editor for the Int. J. of Adaptive Control and Signal Processing; as Senior Editor for the IEEE Trans. on Automatic Control; and as Editor-in Chief for the European Journal of Control. He is currently Editor-in-Chief of the IEEE Trans. on Automatic Control (2018–). He served as Chair of the IEEE CSS Conference Editorial Board (2010-2017) and in the IPC of several international conferences. He has served as Chair of the IEEE CSS Antonio Ruberti Young Researcher Prize (2015-2021); he is Vice Chair of the IFAC Technical Board (2020-2023) and he has been/is a Member of the IEEE Fellow Committee (2016), (2019-2022). He is currently a member of the IEEE PSPB Strategic Planning Committee and of the IEEE Fellow Nomination & Appointment Committee.

Title: Geometry, Coordinates, Invariance and Energy in the Control Of Unmanned Systems

Abstract: Geometry, coordinates, invariance and energy play a key role in the control of unmanned systems. We illustrate their role in the (adaptive) stabilization of underwater vehicles, surface vessels, tractor-trailer systems, unmanned aircraft and magnetically controlled satellites.

Prof. Marios Polycarpou,  University of Cyprus, IEEE Fellow, Member of the Academy of Europe

Marios Polycarpou is a Professor of Electrical and Computer Engineering and the Founder of the KIOS Research and Innovation Center of Excellence at the University of Cyprus. He is also a Member of the Cyprus Academy of Sciences, Letters, and Arts, an Honorary Professor of Imperial College London, and a Member of Academia Europaea (The Academy of Europe).  He received the B.A degree in Computer Science and the B.Sc. in Electrical Engineering, both from Rice University, USA in 1987, and the M.S. and Ph.D. degrees in Electrical Engineering from the University of Southern California, in 1989 and 1992 respectively. His teaching and research interests are in intelligent systems and networks, adaptive and learning control systems, fault diagnosis, machine learning, and critical infrastructure systems. Prof. Polycarpou is the recipient of the 2023 IEEE Frank Rosenblatt Technical Field Award and the 2016 IEEE Neural Networks Pioneer Award. He is a Fellow of IEEE and IFAC. He served as the President of the IEEE Computational Intelligence Society (2012-2013), as the President of the European Control Association (2017-2019), and as the Editor-in-Chief of the IEEE Transactions on Neural Networks and Learning Systems (2004-2010). Prof. Polycarpou currently serves on the Editorial Boards of the Proceedings of the IEEE and the Annual Reviews in Control. His research work has been funded by several agencies and industry in Europe and the United States, including the prestigious European Research Council (ERC) Advanced Grant, the ERC Synergy Grant and the EU-Widening Teaming program. 

Title:  Connecting AI to the Cyber-Physical World: Risks, Opportunities and Challenges

Abstract: The development of cyber-physical systems with multiple sensor/actuator components and feedback loops has given rise to advanced automation applications, including energy and power, intelligent transportation, water systems, manufacturing, etc. Traditionally, feedback control has focused on enhancing the tracking and robustness performance of the closed-loop system; however, as cyber-physical systems become more complex and interconnected and more interdependent, there is a need to refocus our attention not only on performance but also on the resilience of cyber-physical systems. In situations of unexpected events and faults, artificial intelligence and machine learning can play a key role in improving the fault tolerance of cyber-physical systems and preventing serious degradation or a catastrophic system failure. The goal of this presentation is to provide insight into the design and analysis of intelligent monitoring methods for cyber-physical systems, which will ultimately lead to more resilient societies.

肖亮教授，厦门大学，IEEE Fellow

Prof. Liang Xiao,  Xiamen University, China, IEEE Fellow

肖亮：厦门大学教授，入选国家级人才项目，IEEE Fellow，IEEE通信学会杰出讲师，爱思唯尔中国高被引学者。曾担任多个IEEE期刊的编委，包括IEEE TIFS、TCOM、TWC和TDSC等期刊的副编辑，以及IEEE STSP的特邀编辑。主要研究方向为无线安全，隐私保护和无线通信。出版3部英文专著，荣获2024年IEEE通信学会亚太区优秀论文奖，2017年IEEE ICC、2018年IEEE ICCS和2016年IEEE INFOCOM BigSecurity研讨会的最佳论文奖。

题目：面向无人驾驶的车辆协作感知抗数据篡改攻击

摘要：车联网协作感知技术赋能自动驾驶车辆分享点云、图片和行驶状态等感知数据，但在数据篡改攻击下可导致目标虚警和漏报等感知错误。本报告探讨基于强化学习的车辆协作感知方案，提高目标检测任务的感知精度和速度。该方案基于目标空间位置一致性、感知数据分辨率和车辆间信道增益等信息选择协作车辆，最大化感知精度、速度及数据传输最小时延需求加权和的效益函数。此外，介绍了感知数据空间一致性检验机制检测虚假数据，降低如目标位置漏报等感知错误。最后，构建车辆与攻击者之间博弈模型，基于该博弈模型的纳什均衡给出感知精度和速度的性能界，揭示扰动强度、数据量大小及感知数据分辨率对性能的影响机理。

Liang Xiao is an IEEE Fellow and a Professor in the Department of Informatics and Communication Engineering, Xiamen University. She has served in several editorial roles, including an associate editor of IEEE Transactions on Information Forensics & Security, IEEE Transactions on Communication, IEEE Transactions on Wireless Communication and IEEE Transactions on Dependable and Secure Computing, and Guest Editor of IEEE Journal on Selected Topics in Signal Processing. Her research interests include wireless security, privacy protection, and wireless communications. She published three books and three book chapters. She won 2024 IEEE ComSoc Asia-Pacific Outstanding Paper Award, as well as the best paper award for 2017 IEEE ICC, 2018 IEEE ICCS and 2016 IEEE INFOCOM Bigsecurity WS. She was 2022-2023 IEEE ComSoc Distinguished Lecturer.

Title: Collaborative Vehicular Perception for Autonomous Driving Against Data Fabrication Attacks

Abstract: Collaborative vehicular perception enables autonomous connected vehicle (CAV) to share the sensing data such as point clouds, images and driving status, which has performance degradation against data fabrication attacks that share faked sensing data to result in perception errors such as false alarms. In this report, we discuss reinforcement learning-based collaborative vehicular perception scheme to enhance perception accuracy and speed, which chooses collaborative CAVs to enhance the utility as the weighted sum of perception accuracy, speed, and the minimum latency requirement for data sharing. The spatial consistency check is presented to reduce perception errors such as the false positive rate of object locations. The upper performance bound of perception accuracy and speed is provided based on the Nash equilibrium of the game between CAVs and the attacker, revealing the impact of perturbation intensity, data size and point cloud resolution on the perception performance.

田大新教授，北京航空航天大学,长江学者，IEEE Fellow

Prof. Daxin Tian,  Beihang University, China, Changjiang Scholar, IEEE Fellow

田大新：长江学者特聘教授，IEEE Fellow，北京航空航天大学科研院副院长兼前沿创新处处长，“科学探索奖”获得者，国家优青、青年长江、牛顿高级学者、中国工程院首届“中国工程前沿杰出青年学者”，担任中国指挥与控制学会无人系统专委会主任、中国电子学会智能交通信息工程分会副主任、中国计算机学会智能汽车分会副主任、车路协同与安全控制北京市重点实验室主任。发表学术论文100余篇，出版专著7本、教材2本、译著2本，授权发明专利51项；主持国家重点研发计划“揭榜挂帅”重点专项、国家自然基金重点项目等国家项目12项；获国家科技进步奖二等奖等科技奖15项，国家教学成果奖一等奖等教学奖5项。

题目：复杂环境下的全自动驾驶技术

摘要：自动驾驶的终极目标，是让AI系统真正代替人类，实现对复杂环境中驾驶任务的完全自主执行。不同于封闭、可控的任务环境，自动驾驶需持续应对动态、开放、强耦合的交通系统，对其泛化能力、交互理解与自主决策水平提出了更高要求。本报告聚焦车辆自主认知与决策智能，围绕知识获取、联想预判与持续进化三大核心能力，探索融合多模态认知、因果推理与生成学习的端到端自动驾驶系统架构。同时，报告将介绍在多源感知融合、自主决策控制与鲁棒轨迹生成等方面的最新技术进展，为实现面向复杂环境的高智能自动驾驶系统提供思路支撑与方法基础。

Daxin Tian is a distinguished professor and doctoral supervisor, a Changjiang Scholar, and an IEEE Fellow. He currently serves as the Deputy Director of the Research Institute and the Director of the Frontier Innovation Division at Beihang University (BUAA). He is also a recipient of the Scientific Exploration Award, the Excellent Young Scholars Fund from the National Natural Science Foundation, and the Newton Advanced Scholars Fund. Additionally, he has been honored as a Young Changjiang Scholar, an Outstanding Young Scholar in the Frontiers of Chinese Engineering by the Chinese Academy of Engineering. He holds the position of Director at the Beijing Key Laboratory of Vehicle-road Collaborative and Safety Control, as well as the Chairman of the Unmanned Systems Committee of the Chinese Society of Command and Control. Tian has led 12 national-level projects, including the "Challenge-oriented" project of the National Key R&D Program, key projects funded by the National Natural Science Foundation, and international cooperation projects, along with 4 provincial and ministerial-level projects. He has published over 100 academic papers, authored 11 books, and holds more than 50 patents for inventions. He has received 15 awards, including the Second Prize of the National Science and Technology Progress Award.

Title: Fully Autonomous Driving Technology in Complex Environments

Abstract: The ultimate goal of autonomous driving is to enable AI systems to completely replace human drivers in performing driving tasks within complex environments. Unlike closed and controllable task environments, autonomous driving must continuously adapt to dynamic, open-ended, and tightly coupled traffic systems, placing higher demands on generalization capabilities, interaction understanding, and autonomous decision-making levels. This report focuses on vehicle autonomous cognition and decision intelligence, exploring an end-to-end autonomous driving system architecture that integrates multi-modal cognition, causal reasoning, and generative learning, centered around three core capabilities: knowledge acquisition, associative prediction, and continuous evolution. Additionally, the report presents the latest technological advancements in multi-source perception fusion, autonomous decision control, and robust trajectory generation, providing conceptual support and methodological foundations for developing highly intelligent autonomous driving systems capable of operating in complex environments.

汪小帆教授，上海应用技术大学校长

Prof. Xiaofan Wang, President of Shanghai Institute of Technology, China

汪小帆：1996年于东南大学获得博士学位。2002年开始在上海交通大学担任教授，曾任上海交通大学电子信息与电气工程学院副院长和致远学院常务副院长、上海大学副校长，现为上海应用技术大学校长。曾获国家杰出青年科学基金（2002），入选教育部长江学者特聘教授（2008）。曾获国家自然科学二等奖（2015）。现任国际自动控制联合会（IFAC）信息物理制造系统协调委员会副主席、中国系统工程学会副理事长等学术职务。主要研究领域为复杂动态网络分析与控制。

题目：具有执行器饱和的多智能体系统协同控制

摘要：多智能体系统协同控制理论为无人系统集群提供了理论基础，实际应用中往往存在的执行器饱和约束对集群的协同性能构成了挑战。本报告系统性地阐述我们多年来在具有执行器饱和约束的多智能体协同控制领域取得的研究成果。报告从三个关键维度构建分析框架与控制机制：对称与非对称饱和约束避免、半全局与全局饱和协同达成、同质与异质饱和处理。这些研究旨在为克服执行器饱和带来的限制、实现高性能无人集群协同提供理论和方法指导。

Xiaofan Wang received the Ph.D. degree from Southeast University, China in 1996. He has been a Professor with Shanghai Jiao Tong University (SJTU) since 2002 and a Distinguished Professor of SJTU since 2008. He was the vice-president of Shanghai University, and is now the president of Shanghai Institute of Technology, China. He received the 2002 National Science Foundation for Distinguished Young Scholars of P. R. China, the 2008 Distinguished Professor of the Chang Jiang Scholars Program, Ministry of Education, and the 2015 Second Class Prize of the State Natural Science Award, China. He is currently vice-chair of IFAC Coordinating Committee CC5, and vice-chair of Systems Engineering Society of China. His current research interests include analysis and control of complex dynamical networks.

Title: Cooperative Control of Multi-Agent Systems with Actuator Saturation

Abstract: The advancement of cooperative control theory for multi-agent systems has established a theoretical foundation for unmanned system swarms. However, inherent physical constraints frequently induce actuator saturation in practical implementations, presenting a barrier to transitioning cooperative control from theory to practice. This report systematically presents our years of research achievements in the field of multi-agent cooperative control subject to actuator saturation constraints. The presentation will construct analytical frameworks and control mechanisms from three critical dimensions: mitigation of symmetric and asymmetric saturation constraints, achievement of semi-global and global saturated coordination, and treatment of homogeneous and heterogeneous saturation. The research outcomes provide theoretical and methodological guidance for overcoming limitations induced by actuator saturation and enabling high-performance cooperative control of unmanned system swarms.