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由香港科技大学和香港机械工程师学会(HKSME)联合主办，IEEE RAS、韩国国立顺天大学、四川大学、西安交通大学和大连海事大学协办，香港前沿科学研究协会支持的2024年第九届自动化、控制与机器人工程国际会议(CACRE 2024)将于2024年7月18-20日在韩国济州岛举行！
以上日程为简要情况，具体请以会前发送的文件为准
Jing Sun 教授
IEEE Fellow
美国密歇根大学
Jing Sun教授拥有44项美国专利，发表了300多篇存档的期刊和会议论文。她是NAI(美国国家发明家学会)、IEEE(电气与电子工程师学会)、IFAC(国际自动控制联合会)和SNAME(造船与海洋工程学会)的院士。她是2003年IEEE控制系统技术奖的获得者。
报告标题：A Multi-scale Optimization Framework for Integrated Dynamic Systems
摘要：Integrated systems are ubiquitous as more heterogeneous physical entities are combined to form functional platforms. With increased connectivity, new and “invisible” feedback loops and physical couplings are introduced, leading to emerging dynamics and making the integrated systems more control-intensive. The multi-physics, multi-time scale, and distributed-actuation natures of integrated systems present new challenges for modeling and control. Understanding their operating environments, achieving sustained high performance, and incorporating rich but incomplete data also motivate the development of novel design tools and frameworks.
In this talk, I will use the integrated thermal and power management of connected and automated vehicles (CAVs) as an example to illustrate the challenges in the prediction, optimization, and control of integrated systems in the era of rapid advances in AI and data-driven control. While first-principle-based modeling is still essential in understanding and exploiting the underlying physics of the integrated systems, model-based control and optimization have to be used in a much richer context to deal with the emerging dynamics and inevitable uncertainties. For CAVs, we will show how model-based design, complemented by data-driven approaches, can lead to control and optimization solutions with a significant impact on energy efficiency and operational reliability, in addition to safety and accessibility.
刘辛军 教授
中国清华大学
刘辛军，清华大学长聘教授，国家高层次人才入选者、国家重点研发计划项目首席科学家。现任国际机构学与机器科学联合会(IFToMM)中国委员会主席、精密超精密制造装备及控制北京市重点实验室主任，曾担任IEEE CYBER2019、CCMMS 2020、ICIRA2021和IFToMM CCMMS 2022四个国际会议的大会主席。
报告标题：Transformative robotic machining technology and equipment for large-scale structures
摘要：In this talk, the connotation of scientific and technological innovation and its role in the history of human development will be reviewed firstly. Then, the importance of scientific and technological innovation research and equipment innovation and its application in engineering will be emphasized. The scientific and technological innovation research models will be discussed and some examples about processing of complex structures and large structural parts will be given. This talk will focus on the conception, technical verification and engineering application of innovative research and development of industrial robots. Some expectations on the innovative development of robotic equipment will be shared at end of the talk.
Ji-Hong Li 教授
韩国机器人与技术融合研究所
Ji-Hong Li是大韩民国韩国机器人与技术融合研究所的首席研究员。Ji-Hong Li博士目前是韩国普庆国立大学的兼职教授，也是中国科学院沈阳自动化研究所的客座教授。他目前的研究兴趣主要集中在各种海洋机器人的导航、制导和控制方面。他是韩国海洋水产部第26届韩国“海洋日”部长奖获得者，2021年韩国百大杰出研发成果获得者。此外，Ji-Hong Li博士还是韩国海洋机器人技术学会董事会成员和海洋无人系统学会主席，IEEE高级成员和IFAC TC2.3、TC7.2成员。
报告标题：Navigation, Guidance, and Control of Marine Vehicles
摘要：Following a brief overview of some of recent advancements in the navigation, guidance, and control of marine vehicles at KIRO over the past decade, this talk will primarily address the control issue, especially the trajectory tracking problem for a class of underactuated marine vehicles.
Trajectory tracking of underactuated system has been a challenging and also interesting topic in the nonlinear control community over the past decades. Based on the different ways of reference trajectory design, this trajectory tracking problem usually can be classified into two categories: trajectory tracking and path following, where the former one further can be divided into the traditional trajectory tracking and trajectory following. This talk addresses the traditional trajectory tracking problem for a class of underactuated marine vehicles. Compared to the previous related works where the proposed methods usually depended on the vehicle’s specifically simplified dynamics and also needed various restricting conditions on the reference trajectory, in this talk we try to provide a sort of general form of controller with general form of vehicle’s dynamics and also without any restriction on both of the vehicle’s dynamics and the reference trajectory.
Dong Eui Chang 教授
韩国高等科学技术研究院
Dong Eui Chang是KAIST电气工程学院教授、SeongNam KAIST下一代ICT研究中心负责人和KAIST HwaSeong科学中心负责人。2021-2022年，他在韩国总统安全局顾问委员会任职。在2017年加入KAIST之前，他是滑铁卢大学应用数学系的副教授。他是施普林格2018年出版的《数学框架中的深度神经网络》一书的合著者。他的研究兴趣包括控制、机器人、力学和人工智能。
报告标题：Feedback integrators: A new method for structure-preserving numerical integration of dynamical systems
摘要：Structure-preserving numerical integration for ordinary differential equations is very crucial in numerical simulation of dynamical systems. In general, numerical integration of ordinary differential equations is expected to preserve first integrals and state-space manifolds such as energy, angular momentum and SO(3) for the free rigid body dynamics. As such, structure-preserving integration has been a vast research area for which various algorithms have been developed such as symplectic integrators, variational integrators and so on. Most of the algorithms, however, require special tricks, case-by-case, such as solving implicitly defined algebraic equations at each integration step or using a particular parameterization of a given manifold.
In this talk, I will present a new method of structure-preserving integration, called feedback integrators, which does not require any of these special tricks but rather allows one to generally use any off-the-shelf numerical integrators such as the Euler method and the Runge-Kutta method, in order to numerically integrate a given dynamical system while preserving its conserved quantities. Feedback integrators apply to holonomic mechanical systems and non-holonomic mechanical systems as well as regular mechanical systems. They also extend to controller design for systems defined on manifolds. The theory of feedback integrators is still in the making to which everyone is welcome.
赵春晖 教授
中国浙江大学
赵春晖，浙江大学求是特聘教授，华东交通大学学术副校长，为国家杰出青年基金获得者，中国青年女科学家奖获得者，中国自动化学会会士。曾获教育部自然科学奖、浙江省首届青年科技奖等多项省部级奖励。获得中国自动化学会自然科学一等奖、中国自动化学会首届青年女科学家奖等十余项学术奖项。担任《Journal of Process Control》的Senior Editor、《Control Engineering Practice》、《Neurocomputing》等三本国际期刊和《控制与决策》等三本国内期刊的副编。
报告标题：Theoretical exploration and practice of industrial process fault diagnosis based on zero sample learning
摘要：Fault diagnosis system is an important guarantee for the safe and reliable operation of industrial processes. Data-driven fault diagnosis modeling often depends on the collected sufficient historical fault data. However, in actual industrial processes, it is common that process failures have no historical samples and no labels. In this regard, we need to deal with a very challenging fault diagnosis task, that is, to consider diagnosing when there are no historical fault samples available for model training. We introduced the concept of zero-shot learning into the industrial field for the first time, and innovatively established a zero-shot-learning fault diagnosis method. By skillfully introducing a priori modeling knowledge with fault description as the carrier and adopting the attribute migration method, we overcame the concerned bottleneck problem that traditional fault diagnosis research cannot meet the sample size constraint. We theoretically analyzed and explained the effectiveness and feasibility of the zero-shot-learning diagnosis method based on fault description. In addition, the fault diagnosis performance in the case of zero samples is verified in the real megawatt thermal power process, and the results show that it is feasible to diagnose unseen target fault without samples. On this basis, the existing challenges, difficulties and future research directions are revealed.
高岳 副教授
中国上海交通大学
上海交通大学电子信息与电气工程学院人工智能研究院，副教授、博士生导师，获上海市高层次人才青年计划。研究方向为足式机器人行为智能。担任国家重点研发计划课题负责人、国家自然科学基金“共融机器人”重大研究计划集成项目课题、国家自然基金面上和青年项目负责人，担任Robotica杂志副主编。获世界人工智能大会卓越人工智能引领者奖、全国高校教师教学创新大赛一等奖。
报告标题：Advancements in Learning-Based Control: Transforming the Mobility of Legged Robots and Their Real-World Applications
摘要：Legged robots have a broad spectrum of applications, primarily due to their adaptation when traversing unstructured environments. Their complexity arises from diverse configurations such as bipedal, quadrupedal and hexapedal, extending to control models as multi-input, multi-output, multi-end-effector systems. This complexity introduces significant challenges in locomotion control and planning, including model identification, real-time computational demands, and adaptation to unseen tasks. The purpose of this talk is to provide an overview of our new research in learning-based control for legged robots. Leveraging advancements in reinforcement learning and deep learning, we have significantly enhanced legged robots' mobility, safety, and the ability of self-learning. Furthermore, this talk will highlight an interesting application of our research through the curling and skiing six-legged robots designed for Beijing Winter Olympics, demonstrating the real-world application potentials for learning-based control for legged robots.
周雨旸 博士
英国爱丁堡龙比亚大学
周雨旸于2018年博士毕业于英国曼彻斯特大学，现任爱丁堡龙比亚大学讲师，硕士和博士生导师。主要研究方向为随机控制理论，卡尔曼滤波，复杂系统控制，多智能体系统控制，以及分布式控制。曾在2018年到2021年之间在英国阿斯顿大学担任博士后研究员。在控制领域国际顶刊IEEE TAC和AUTOMATICA，以及国际知名学术期刊和会议上发表论文30余篇，担任Mathematics、Entropy等国际知名控制领域期刊客座编辑。
报告标题：Fully Probabilistic Control Algorithm Design for a class of Complex Stochastic Systems
摘要：Complex dynamical systems have garnered significant interest in the realms of control and engineering, as they offer a cohesive and natural framework for the mathematical modeling of diverse real-world systems, such as communication networks, power grids, and chemical processes. The inherent characteristics of these systems, such as high dimensionality, intricate structures, complex models with multiple modes of switching, and substantial uncertainties, pose notable challenges for system analysis, estimation, and particularly for control.
This talk will introduce a novel decentralised probabilistic control framework designed for complex stochastic systems. It will offer a fresh research outlook dedicated to refining the control of such systems. In this talk, we will delineate the core principles and applications of the framework, showcasing its contribution to the development of innovative control strategies for complex stochastic systems.
曹老师
19150956004
cacre@vip.163.com
www.cacre.org
陈老师
19160368706（微信同号）
