特邀论坛一
Modeling, Analysis, Numerical Simulation of the Delayed Stochastic Systems with Applications
邓飞其 教授
华南理工大学，中国
摘要
The understanding of the talker about the stochastic phenomenon and the stochastic system models, as well as the recent progresses achieved by his group about the research on the theory of the delayed stochastic systems, will be introduced, including: 1. Those on the fundamental theories, e.g. the nonnegative semi-martingale boundedness lemma, the improvement on the Doob’s martingale inequality, the implication theorem for the stability of the delayed stochastic systems; 2. Some interesting discoveries on the control theory of the delayed stochastic systems, e.g. the reachability the states of the delayed stochastic systems to zeros, the stabilizability of some uncontrollable systems by delayed feedbacks, the stabilization of the stochastic systems by feedbacks with unbounded or over large time delays; 3. Some progresses on the control theory of the delayed stochastic systems, e.g. the divided feedback stabilization of the stochastic systems, the accurate numerical schemes of the stochastic systems with sampled data with applications, the stability of the networked control systems. Besidess, some challenges will be presented, e.g. the event-triggering control of the stochastic systems, the boundedness of the solutions of the stochastic systems by deterministic bounds, etc. Communications on related topics with the participants are expected.
个人简介
邓飞其，1983年毕业于湖南大学应用数学专业，获学士学位，1997年毕业于华南理工大学自动控制专业，获工学博士学位。历任华南理工大学工业技术研究总院院长、中国民主同盟中央委员会委员、广东省委副主委、广东省政常委，广东省人民政府参事、华南理工大学学术委员会委员、中国仿真学会不确定性系统分析与仿真专业委员会副主任委员等职，现为华南理工大学系统工程研究所所长、IFAC Technical Committee on Stochastic Systems主席、IEEE Technical Committee on the Cybernetics of the Complex Systems主席、IEEE CSS Guangzhou Chapter主席、中国自动化学会会士、中国自动化学会控制理论专业委员会（TCCT）委员、关肇直奖评奖委员会委员、广东省物联网技术标准委员会主任、TCCT随机系统控制学组主任，中国仿真学会不确定性系统分析与仿真专业委员会委员；主要研究复杂系统控制理论与智能技术，主持国家基金重点项目，出版专著4部，发表论文600多篇；获得2002年度国务院特殊津贴，获得第七届丁颖科技奖；获得教育部自然科学二等奖，广东省自然科学二等奖；连续入选全球前2%顶尖科学家、全球高排名学者榜单；2016、2021、2025年度中国自动化学会优秀博士论文奖指导教师。
Applications of the Fully Actuated System Theory on the Nonlinear Control of Robots and UAVs
刘万泉 教授
中山大学，中国
摘要
In this seminar, I will establish some novel strategies for robot control within the recently proposed fully actuated system framework. Some critical challenges, including predefined-time consensus, input constraints, nonholonomic constraints, and heterogeneous cooperation, are systematically addressed. Firstly, for omnidirectional mobile robots operating in complex environments, a predefined-time consensus control scheme is developed under input constraints, nonholonomic constraints, and collision-free formation requirements. A novel fully actuated predefined-time behavioural control strategy is developed by integrating a predefined-time cooperative protocol with a null-space-based behavioural control mechanism, thereby enabling distributed formation control. Secondly, for tendon-driven manipulators, a fully actuated control framework based on model predictive control (MPC) is established to address input constraints, strong coupling, and vibration induced by high-frequency torque variations. In both cases, by exploiting the fully actuated transformation, complex nonlinear dynamics are reformulated into a tractable linear form, significantly reducing controller design complexity. Finally, for heterogeneous air–ground cooperative systems, a unified fully actuated modelling and control approach is developed to address dynamic discrepancies among heterogeneous robots and to reduce reliance on accurate nonlinear models. By transforming the dynamics of unmanned aerial vehicles and ground vehicles into a unified, fully actuated representation, distributed consensus-based formation control can be achieved, resulting in better coordination accuracy and system stability. In summary, a unified fully actuated system control framework is established for some typical robot control problems, to systematically address critical issues brought by the nonlinear complexity.
个人简介
刘万泉博士： 中山大学智能工程学院教授，国家特聘专家。1985年获曲阜师范大学数学学士学位，1988年获中科院系统科学研究所运筹学与控制论硕士学位，1993年获上海交通大学博士学位。1993年至2021年在澳大利亚西澳大学，悉尼大学和科挺大学工作28年。曾获悉尼大学U2000研究员，澳大利亚基金委杰出研究员（ARC Fellow）和日本JSPS高级研究员多项科研人才称号。2015年获北京市海聚人才计划，2018年获镇江市2025智能制造领军人才。 2021年获中山大学百人计划领军人才， 同年获深圳市孔雀人才计划。 2022年获广东省珠江领军人才与国家特聘专家。 他在国际知名杂志与重要会议发表论450多篇。担任国际杂志Mathematical Foundation of Computing 的主编与多个国际杂志的编委。 他目前的研究方向是机器学习，智能控制与智慧养老与康复。
Structure Identification for Networked Dynamic Systems under Slow and Non-uniform Samplings
周彤 教授
清华大学，中国
摘要
随着技术的进步和对性能要求的提高，系统规模日益增大。另外，随着人工智能等领域的发展，人造大规模网络层出无穷。然而，这类系统的可控/可观/因果性判断、结构辨识等基本问题依然未能得到很好解决。本报告引入一种基于输出连接的大规模网络化系统描述方式，证明其集总式系统矩阵以线性分式变换的形式依赖于其子系统连接矩阵。另外，已有研究表明，元素是系统参数有理函数的任意系统矩阵，都可表示为这些参数的线性分式变换。本报告将给出网络化系统结构可辨识的判断方法；引入“参数松懈性”的概念，推导出其度量和计算方法；建立一个系统稳态响应与其传递函数阵取值之间的线性关系；提出一种不依赖于采样间隔和速率的网络化系统结构估计方法，讨论其在非同步采样、非线性网络化系统的推广。最后，通过简单的质量-弹簧-阻尼器系统等，说明该辨识算法的特点及其在集总式连续系统参数估计上的应用。
个人简介
周彤，1984年7月于成都电讯工程学院获自动控制工学学士学位，1988年7月于电子科技大学获自动控制理论与应用工学硕士学位，1991年3月于日本国金沢大学获电气与电子工程工学硕士学位，1994年3月于日本国大阪大学获产业机械专业工学博士学位。现为清华大学自动化系工业智能与系统研究所教授、博士研究生导师。主要研究领域为，网络化系统建模与控制、鲁棒控制与滤波、面向控制的系统辨识、磁悬浮技术、基因调控网络建模与分析等。研究工作得到了“教育部优秀青年教师资助计划” （2001年度）、“教育部跨世纪优秀人才培养计划” （2002年度）、“国家自然科学基金杰出青年基金”（2006年度）等人才基金的支持。研究结果获教育部提名国家科学技术奖自然科学奖一等奖（2003年度），中国自动化学会自然科学奖一等奖（2020年度）。 曾担任 IEEE Transactions on Automatic Control 的 Associate Editor（连续二届）。目前担任 Automatica 的Associate Editor（连续五届）。中国自动化学会会士, IEEE Fellow。
Analysis and Control of Stochastic Nonlinear Systems
徐胜元 教授
南京理工大学，中国
摘要
This talk focuses on the multi-faceted, coupled challenges including stochastic disturbances, inherent nonlinearities, and signal time delays encountered by complex equipment such as high-precision fire control systems. Centering on three core frontier issues in the control of stochastic nonlinear systems--the complete elimination of nonlinear growth conditions, the effective reduction of controller complexity, and the transformative enhancement of system dynamic performance, the talk systematically elucidates the latest theoretical breakthroughs in this field. It establishes a comprehensive stability theory framework designed to guarantee the rapid and precise convergence of such systems, aiming to provide the theoretical framework and methodological support necessary for the construction of high-performance stochastic control systems.
个人简介
徐胜元, 国家杰出青年科学基金获得者，教育部长江学者特聘教授，教育部创新团队学术带头人，基金委创新研究群体项目负责人，2019年度国家自然科学二等奖获得者。
1990年于杭州师范学院获理学学士学位，1996年于曲阜师范大学获理学硕士学位，1999年于南京理工大学获工学博士学位。2000年12月至2001年11月在比利时鲁汶大学做博士后研究，2001年12月至2002年9月在加拿大艾尔伯特大学做博士后研究，2002年9月至2003年9月获聘为香港大学William Mong青年研究员。现为南京理工大学自动化学院教授，博士生导师。
Hyperexponential stability and stabilization for uncertain nonlinear systems
刘允刚 教授
山东大学，中国
摘要
This talk discusses hyperexponential stability and hyperexponential stabilization for uncertain nonlinear systems. A definition of hyperexponential stability is first given in the widely-recognized manner. Its connections with the existing definitions of hyperexponential stability are identified. Necessary and sufficient conditions are then established to demonstrate the possibilities and impossibilities of pursuing hyperexponential stability. A time-varying-gain-based strategy is finally developed to achieve global hyperexponential stabilization for a class of uncertain nonlinear systems.
个人简介
山东大学二级教授、院学术委员会副主任、教育部重点实验室主任。长江学者特聘教授、国家杰出青年科学基金获得者、山东省泰山学者攀登计划入选者。曾获国家自然科学奖二等奖、第十届关肇直奖、中国自动化学会优秀博士学位论文指导教师奖、山东省优秀研究生指导教师奖、山东大学优秀教师奖、山东大学“我心中的好导师”等。现/曾任中国系统工程学会理事、中国人工智能学会自主无人系统专委会副主任委员、山东省电子学会常务理事及人工智能与机器视觉专委会主任等。主要研究领域：非线性控制、自适应控制、随机系统优化与控制、智能系统理论与方法、基于数据的智能控制方法与技术等。主持国家自然科学基金重点项目、山东省重大科技创新工程项目、山东省重大基础研究项目等。
特邀论坛二
From Artificial Intelligence to Embodied Intelligence
刘勇 教授
浙江大学控制科学与工程学院，中国
摘要
Embodied intelligence has promising application potential across various fields, including industrial manufacturing, autonomous driving, logistics and transportation, household services, medical care and elderly health, among others. This report will briefly review the historical evolution from the boom of artificial intelligence to the emergence of embodied intelligence, and outline its core components and implementation approaches.
个人简介
刘勇，浙江大学控制科学与工程学院教授，浙江大学控制学院智能驾驶与未来交通中心主任，浙江大学-天数智芯先进智能计算联合研发中心主任，浙江大学先进智能系统研究中心副主任，浙江大学控制科学与工程学院党委委员，浙江省机器换人专家。承担国家自然科学青年科学基金项目（A类），获中国专利银奖、浙江省自然科学一等奖、浙江省科学技术一等奖、浙江省科学技术进步一等奖、浙江省知识产权专利奖一等奖、浙江省自然科学学术二等奖、浙江省杰出青年科学基金项目，入选中组部万人计划青年拔尖人才、浙江省有突出贡献青年科技人才、2022年杭州市钱江特聘专家和浙江省 151 人才项目，以第一作者或通讯作者在TPAMI、TRO、IJCV、JMLR、TIP、CVPR、ICCV、ECCV、NeurIPS、ICLR、ICRA、IROS等知名期刊和机器人/计算机视觉顶级会议发表论文百余篇。主要研究方向为：自主机器人与智能系统、机器人自主规划与导航控制、视觉识别与模式识别、SLAM技术及多传感器融合技术。
面向复杂环境的仿海胆机器人机动技术
赵旭东 教授
大连理工大学，中国
摘要
针对抢滩登陆中浅滩、礁石等复杂地形环境下隐蔽侦察任务的机动需求，发挥仿生类无人系统高隐蔽性和“不怕伤亡”天然优势，及可快速部署并进入到有人作战力量无法或不便涉足的危险、恶劣环境和空间中等特点。开展自适应类海胆仿生机器人总体技术研究和基于深度学习和自主学习的控制研究，研制原理样机并开展实验验证。本报告将介绍仿海胆机器人研发的几个研究结果，主要包括：设计灵感及硬件结构设计、基于动力学的运动控制、基于强化学习的运动控制、 仿真及样机实验。
个人简介
赵旭东，大连理工大学教授，博士生导师， “工业装备智能控制与优化教育部重点实验室”副主任。入选国家级领军人才支持计划。近年来在切换系统、不确定系统、几类非线性系统的稳定性、鲁棒控制、智能控制及其在航空发动机、机器人的应用等领域取得了一系列的研究成果。在Automatica及IEEE Transactions系列汇刊发表论文160余篇，其中包括控制领域顶级期刊Automatica和IEEE TAC 20余篇。 科研成果被引用20000余次，多篇学术论文入选ESI-TOP高被引论文。主持国家科技重大专项（首席科学家），国家重点研发计划项目，国家自然科学基金重点项目，国家优青项目，JKW某工程项目，两机重大专项课题等多个重要项目。获得全球高被引科学家奖（Web of Science）、USERN青年科学家奖、中国自动化学会青年科学家奖；获得教育部自然科学二等奖、中国自动化学会自然科学一等奖等科技奖励8项；出版英文专著2部；授权国家发明专利10余项。担任中国指挥与控制学会智能控制与系统专业委员会常务委员、集群智能与协同控制专业委员会常务委员，中国空天动力联合会发动机控制技术专业委员会委员，中国机械学会机器人专业委员会委员。担任《自动化学报》、《控制工程》及SCI期刊《IEEE Transactions on Systems, Man and Cybernetics: Systems》、《Nonlinear Analysis: Hybrid Systems》等编委工作,同时担任《Engineering Reports》顾问委员会委员。
城市污水处理多目标动态协同优化
韩红桂 教授
北京工业大学，中国
摘要
城市污水处理是保护环境、实现水资源循环利用的有效途径，然而，由于城市污水处理过程具有多流程、多工况、时变等特性，基于单一尺度、单一层次、单一目标的优化控制不能保证整体运行的最优。城市污水处理过程多目标协同优化控制通过构建不同时间尺度的性能指标，设计多冲突目标动态优化方法，攻克城市污水处理过程多目标协同优化控制技术，实现城市污水处理过程局部与整体之间、短期与长远之间、效益与安全之间的多目标优化，解决了城市污水处理过程关键变量的实时动态优化设定难题，有效降低了城市污水处理过程运行成本。
个人简介
韩红桂，教授、博士生导师，计算机学院院长。长期从事复杂系统智能控制研究，先后入选国家自然科学基金杰出青年基金项目、国家自然科学基金优秀青年基金项目、青年北京学者、中国自动化学会青年科学家、北京高校卓越青年科学家等。研究成果发表学术论文100余篇，撰写著作5部；获得授权中国/美国发明专利60余项；主持/参与制定国家/团体/地方标准10余项。获国家科学技术进步二等奖、教育部科技进步一等奖、吴文俊人工智能科学技术进步奖一等奖等。现任“数字社区”教育部工程研究中心主任、“计算智能与智能系统”北京市重点实验室主任；兼任中国科学：技术科学、IEEE Transactions on Cybernetics等期刊编委。
Multi-source Fusion-based Intelligent Localization and Perception for UAVs
刘准钆 教授
西北工业大学，中国
摘要
Unmanned aerial vehicle (UAV) autonomous navigation and target perception play critical roles in mission execution. In visual navigation, differences in image viewpoints and modalities make image matching and localization highly challenging. In target perception, long-range targets often exhibit weak visual characteristics and are embedded in complex backgrounds, making timely detection difficult. This report introduces an intelligent cross-modal image matching method based on multi-source information fusion for images captured from different viewpoints, aiming to improve the localization accuracy of UAV visual navigation. In addition, a multi-feature information fusion approach for weak and small target detection is presented to enable rapid detection and recognition of long-range targets in complex environments.
个人简介
刘准钆，西北工业大学自动化学院教授/博导，国家杰出青年科学基金获得者，主要从事智能信息融合、目标识别跟踪、导航制导等研究。主持国家自然基金联合基金重点项目等，获陕西省自然科学一等奖，中国人工智能学会自然科学一等奖，中国航空学会青年科技奖等，担任IEEE TCYB、中国科学：信息科学、航空学报编委，中国航空学会理事等。
Direct closed-loop data-driven design for LQ control with unknown dynamics
游科友 长聘教授
清华大学，中国
摘要
Direct data-driven design for the linear quadratic regulator (LQR) typically relies on offline or episodic data batches, leaving online adaptation an open challenge. In this work, we propose a direct adaptive method for learning the LQR from online closed-loop data. First, we introduce a policy parameterization based on the sample covariance, leading to a direct data-driven LQR formulation that is equivalent to the certainty-equivalence LQR and enjoys optimal guarantees. Second, we develop a data-enabled policy optimization (DeePO) method that directly updates the policy using only a batch of persistently exciting data, with an explicit gradient computation. Third, we establish global convergence through a projected gradient dominance property. By performing one-step projected gradient descent per closed-loop sample, DeePO enables adaptive LQR learning via an explicit recursive policy update. Under bounded noise, the regret achieves the optimal rate of plus a bias term that decays inversely with a newly defined signal-to-noise ratio. We further extend this framework to the linear quadratic tracking (LQT) problem, showing that DeePO applies directly while preserving the same algorithmic structure, convergence guarantees, and regret bounds. Simulations validate the theoretical findings and demonstrate the efficiency of the proposed approach for both regulation and tracking tasks.
个人简介
清华大学自动化系长聘教授、博士生导师，国家杰出青年科学基金获得者。2007年获中山大学统计科学学士学位，同年8月至2012年6月于新加坡南洋理工大学电气与电子工程学院攻读博士学位并从事博士后研究。2012年7月起任教于清华大学自动化系，曾受邀访问意大利都灵理工大学、澳大利亚墨尔本大学、香港科技大学等多所高校。长期致力于复杂网络化系统的学习、优化与控制研究。现任Automatica, IEEETransactions on Control of Network Systems等国际期刊副编委，主持国家自然科学基金杰青与重点项目、科技创新2030"新一代人工智能"重大项目等。获中国自动化学会自然科学一等奖(排第一)、亚洲控制学会Temasek青年教育者奖、关肇直最佳论文奖，参与获北京市、教育部、自然资源部等省部级一等奖3项。指导博士生获 IFAC Young Author Award、中国自动化学会优秀博士学位论文奖、国家博士后创新人才支持计划等荣誉。
特邀论坛三
Robust Optimal Adaptive Control for Fully Actuated Uncertain Nonlinear Systems with Applications
顾国祥 教授
Louisiana State University
摘要
We investigate adaptive control for a class of fully actuated uncertain nonlinear systems under state feedback. It is shown that adaptive control of such a class of nonlinear systems is hinged on the linear part. New results include:
• Global asymptotic stabilization (GAS).
• Input-to-state stability (ISS) and exponential stability (ES).
• Output regulation (OR) with full information (FI) and LQR control.
• Robust control and disturbance rejection under H∞-framework.
• Applications to platoon control in achieving disturbance string stability (DSS).
The results are illustrated by simulation studies.
个人简介
Guoxiang Gu (F’10) received the Ph.D. degree in Electrical Engineering from University of Minnesota, Minneapolis, MN, USA, in 1988. From 1988 to 1990, he was with the Department of Electrical Engineering, Wright State University, Dayton, OH, USA, as a Visiting Assistant Professor. He has held visiting positions with Wright-Patterson Air Force Base, OH, USA, and with the Hong Kong University of Science and Technology, Hong Kong. He worked for Louisiana State University (LSU), Baton Rouge, LA, USA, and was a Professor of Electrical and Computer Engineering and F. Hugh Coughlin/CLECO Distinguished Professor of LSU. He has authored two books, over 90 archive journal papers, and numerous book chapters and conference papers. His research interests include networked control systems, modeling and identification, and industrial applications. Dr. Gu served as an Associate Editor for IEEE Transactions on Automatic Control from January 1998 to December 2001 and from January 2018 to December 2021, SIAM Journal on Control and Optimization from 2006 to 2009, and Automatica from 2006 to 2012. He is currently a Professor of Xinan Jiaotong University, an Emeritus Professor in ECE of LSU and Fellow of IEEE.
Identification and control of finite-information systems
张纪峰 研究员
中原工学院/中科院数学院，中国
摘要
The identification and control of limited-information systems is an emerging frontier in control theory in the age of intelligent systems. It addresses a class of fundamental problems arising in major applications such as low-bit high-performance communications, high-precision control of advanced devices, and infrared sensing data fusion. This research direction, recently initiated by the speaker and collaborators, focuses on identification and control under severely constrained data conditions, including binary (0/1) information loss and low-bit quantization. Motivated by strategic demands in future communications, advanced engineering systems, and multi-scale infrared sensing, modern system design is increasingly confronted with a fundamental dilemma: practical constraints on hardware capability, energy consumption, and communication bandwidth often lead to data that are low-precision, distributed, and subject to multiple uncertainties, while real applications demand simultaneously high accuracy, high reliability, and low energy consumption. This creates a profound conflict between limited information and extreme performance, posing challenges that cannot be adequately addressed by traditional control and information theories. This report presents the speaker’s recent research progress in the theory of identification and control under limited-information constraints, including quantized identification under multiple uncertainties, quantized identification and consensus control in distributed network environments, as well as applications of the related theory in communications, engineering, detection, and other fields. Finally, the report briefly discusses the relationship between limited-information system theory and traditional control theory, artificial intelligence, and communication theory.
个人简介
张纪峰，中原工学院学术副校长、中国科学院数学与系统科学研究院研究员。研究方向为随机系统、有限信息系统、多自主体系统的分析与控制等。曾两次获国家自然科学二等奖，现为IEEE Fellow、IFAC Fellow、欧洲科学与艺术院院士、中国自动化学会会士、中国工业与应用数学学会会士。先后任中国科学院数学与系统科学研究院系统所所长，国务院学位委员会系统科学评议组召集人，国际自动控制联合会技术局副主席，中国自动化学会、中国数学会和中国系统工程学会等的副理事长，十多个国内外重要学术期刊的主编、副主编或编委，是科普期刊《系统与控制纵横》的创刊主编。先后主持国家杰出青年基金项目、重点项目，以及科技部重点研发项目、973课题等。
观测能力定量表征与自主导航
王大轶 研究员
北京空间飞行器总体设计部，中国
摘要
Autonomous navigation and autonomous diagnosis/reconfiguration are two key cores for the autonomous operation of spacecraft, among which autonomous navigation is the prerequisite for achieving fully autonomous operation. Focusing on unmanned systems under stringent resource constraints, the presenter takes the quantitative characterization of observation capability as the theoretical innovation breakthrough, and the optimized construction and information selection of sequential images as the key technical breakthroughs. A sequential-image-based autonomous navigation technology driven by quantitative observation capability characterization is proposed, contributing to the safe and reliable autonomous operation of spacecraft.
个人简介
王大轶 研究员，北京空间飞行器总体设计部 科技委主任，是国家杰青、国防卓青、万人领军，973项目技术首席专家、政府特殊津贴专家。长期从事观测、诊断和重构能力定量表征理论方法以及空间飞行器自主运行技术研究，入选国家自然科学基金十三五优秀成果，为我国深空探测、北斗三号等任务成功做出重要贡献。获首届国家卓越工程师奖、国家技术发明二等奖、国家科技进步特等奖、何梁何利基金科技创新奖、全国创新争先奖、钱学森杰出贡献奖等，是国家有突出贡献中青年专家。
UAV Swarm Inspired by Bird Flock Intelligence Incentive and Convergence
段海滨 教授
北京航空航天大学，中国
摘要
Nature is a rich source of human creativity. On the basis of a series of flight experiments on bird flock behavior in nature, the internal mechanism of different behavior phenomena of birds is analyzed through collecting and processing the experimental data of different flight behavior of birds, and the bird flock intelligence incentive and convergence behaviors are modeled. The positive and negative feedback mechanism model of bird flock intelligence emergence is constructed. The mapping relationship between the bird flock intelligence incentive and convergence and the flight of unmanned aerial vehicles (UAVs) is studied. Inspired by the different behavior models of bird flock intelligence incentive and convergence, the UAV cooperative searching method based on the bird flock intelligence incentive and the UAV swarm countermeasure method based on the bird flock intelligence convergence are proposed. The recent progresses in incentive and convergence in bird flock intelligence will also be highlighted.
个人简介
段海滨，中国自动化学会会士、中国人工智能学会会士。国家杰出青年科学基金获得者，教育部长江学者特聘教授，“万人计划”-科技创新领军人才、首批青年拔尖人才。主要从事无人机集群仿生自主飞行控制及应用研究。
主持国家自然基金重大研究计划重点项目、国家自然科学基金原创探索计划重点项目、国家自然科学基金叶企孙科学基金、国家自然科学基金企业创新联合基金重点项目、国家自然科学基金重点项目、国家杰出青年科学基金、军委科技委创新特区项目、装备预研等课题。发表SCI论文90余篇，专著4部，发明专利51项，2020-2025年爱思唯尔中国高被引学者，获吴文俊人工智能自然科学一等奖、CAA自然科学一等奖、CAA技术发明一等奖、中国航空学会科学技术一等奖、吴文俊人工智能科技创新一等奖、国防技术发明二等奖、国防科技进步二等奖、北京市科学技术奖自然科学二等奖(均排名1)，高等教育国家级教学成果二等奖(排名2)、高等教育国家级教学成果一等奖(排名5)。获中国青年科技奖、全国优秀科技工作者、中国青年五四奖章、茅以升北京青年科技奖、中国自动化学会首届青年科学家奖、杨家墀科技奖、冯如航空科技精英奖。《Guidance, Navigation and Control》创刊主编(入选2022中国科技期刊卓越行动计划高起点新刊，E-SCI期刊)，《自动化学报》和《工程科学学报》副主编，《IEEE Trans Cybernetics》、《IEEE Transactions on Circuits and Systems I: Regular Papers》、《IEEE Transactions on Circuits and Systems II: Express Briefs》、《中国科学：信息科学(中、英文版)》、《中国科学：技术科学(中、英文版)》、《智能系统学报》等编委，IFAC TC(自主智能系统委员会) 7.5委员、中国自动化学会无人飞行器自主控制专业委员会主任、中国航空学会制导导航与控制分会主任。
Data-driven control of networked systems
孙健 教授
北京理工大学，中国
摘要
With the development of information technologies, control systems are becoming more intelligent and interconnected. Accurate modeling of a control system has become increasingly difficult. For systems that are difficult to accurately model, traditional model-based control theories and methods are difficult to achieve ideal control performance. Data-driven control refers to the control method of designing controllers based solely on the offline/online data when the mathematical model and parameters of the control system are unknown. Data-driven control methods are independence of precise models and have broad applications. This talk will introduce the recent progress of data-driven control methods for networked systems, including data-driven event-triggered control and self-triggered control, data-driven resilient control under DoS attacks, data-driven self-triggered control based on trajectory prediction, data-driven robust LQG control, and data-driven output regulation of networked systems.
个人简介
孙健，北京理工大学自动化学院教授、院长，自主智能无人系统全国重点实验室常务副主任。主要研究方向为自主智能无人系统、网络化系统，控制系统安全性等。在IEEE汇刊、Automatica等刊物上发表学术论文200余篇，出版学术专著3部。获国家自然科学二等奖1项、教育部自然科学一等奖2项、军队科技进步一等奖1项、国防科技进步二等奖2项。2019年获国家杰出青年科学基金。现任第八届教育部科技委委员，教育部高等学校自动化专业教学指导委员会委员，亚洲控制协会执行委员会委员，亚洲控制协会无人系统专业委员会主任，中国自动化学会控制理论专业委员会副主任、中国自动化学会工业控制系统信息安全专业委员会副主任、中国指挥与控制学会集群智能与协同控制专业委员会副主任、中国指挥与控制学会网络科学与工程专业委员会副主任，《IEEE/ASME Transactions on Mechatronics》《IEEE Transactions on Systems, Man and Cybernetics: Systems》《Science China Information Sciences》《Journal of Systems Science and Complexity》《自动化学报》《电子学报》等刊物编委。
特邀论坛四
Deep learning methodology development for multimodal molecular data analysis
沈红斌 教授
上海交通大学，中国
摘要
With the rapidly increasing protein and related molecular data, understanding the hidden knowledge behind the data, and revealing the hidden relationship between different sources of data is one of the promising interdisciplinary research directions of computer science and life sciences. Multimodal information of molecular sequence, structure and other modal data complement each other. Developing efficient multimodal pattern embedding and data-driven deep learning and pattern recognition algorithms is helpful for speeding up the understanding of molecular big data.
个人简介
上海交通大学特聘教授，曾获国家杰出青年科学基金、万人计划青年拔尖人才资助，曾担任中国人工智能学会生物信息学与人工生命专业委员会副主任、中国科学信息科学青年编委、Genome Biology客座编委。主要从事人工智能与生命科学交叉的教学研究工作，发表学术论文200 余篇，授权发明专利30余项。构建的生物信息人工智能计算平台被广泛使用超过1千万次，理论预测结果被生物实验大量验证，揭示与促进了相关生命知识的新发现。曾获上海市青年科技杰出贡献奖、霍英东教育基金会青年教师奖等，指导毕业的研究生入选国家级青年人才、上海市优秀研究生学位论文奖、上海交通大学优秀博士学位论文奖等多人。
精密光机电定位系统关键技术与应用
胡庆雷 教授
北京航空航天大学，中国
摘要
本报告从典型光机电系统出发，围绕实际应用需求阐明精密定位系统的一般发展趋势及基本矛盾；结合精密定位系统的构成及系统各部分与具体性能的影响分析，探讨提高精密定位系统行程、带宽、精度等重要指标的关键技术。具体从精密机电系统的机构设计与优化、微纳尺度传感与测量、精密伺服跟踪控制三个方面展开，结合课题组近年来开展的相关项目研究工作，针对压电、电磁驱动快速反射镜与复合轴光机跟瞄仪的伺服机构设计与优化、纳米级电容传感器样机的研制及跨尺度传感器信息融合、压电执行器非线性动力学建模与精密伺服控制等具体问题展开分析，探讨在实际应用中面临问题的解决方案。
个人简介
北京航空航天大学自动化科学与电气工程学院教授、入选国家级领军人才。主要从事导航、制导与控制、空间智能感知与操控等研究工作，先后承担国家自然科学基金重大科研仪器研制项目、科技部重大专项课题、装备预研重点项目、国防基础重点项目等20余项，在Automatica、IEEE汇刊、AIAA系列期刊上发表学术论文100余篇，出版学术专著3部，授权国家发明专利60余项，获国家技术发明二等奖、国防技术发明一等奖等科研奖励。担任Aerospace Science and Technology等SCI期刊的编委。
From Model-Free Self-Learning Control to Multi-Agent Decision Optimization
王友清 教授
北京化工大学，中国
摘要
In the control and optimization of complex systems, model-free characteristics, uncertainties, and external disturbances are ubiquitous, posing significant challenges to traditional optimal control methods that rely on accurate system models. In recent years, researchers have explored theories and methods of self-learning and optimization from various perspectives. On the one hand, efforts focus on model-free optimal control at the control level to achieve efficient system operation; on the other hand, they extend to game-theoretic optimization at the decision level to characterize competitive and cooperative behaviors in complex environments.
At the control level, we systematically investigate an adaptive dynamic programming (ADP) approach based on the internal model principle to address the output regulation problem for both linear discrete-time and continuous-time systems. By introducing an improved algorithm based on state reconstruction, we overcome the limitation of unknown exosystem parameters, significantly enhancing robustness and practical applicability. This framework is further applied to power electronic systems, enabling independent regulation of active and reactive power in virtual synchronous generators (VSGs) and ensuring safe operation of Buck converters.
At the decision level, we investigate line-of-sight pursuit-evasion games, a class of adversarial optimization problems with geometric constraints. For line-of-sight pursuit-evasion games in environments with multiple obstacles, we construct state value functions based on geometric modeling and derive optimal strategies via Nash equilibrium. Furthermore, by integrating geometric modeling with differential game theory, we solve for optimal strategies in corner-obstacle scenarios with attack range, thereby enhancing the decision-making capability of agents in complex environments.
个人简介
王友清，国家杰出青年基金获得者、IET Fellow、中国自动化学会会士，北京化工大学教授/博导，信息科学与技术学院院长。王友清担任9个SCI期刊的编委或客座编委，还是3个IFAC技术委员会的委员。他荣获教育部、北京市、山东省自然科学奖各一项。出版专著3本，以第一或通讯作者发表SCI论文160余篇。获得20余项发明专利授权，研究成果在中石化、中石油等头部企业得到应用。论文被SCI引用6000多次。引用者包括40余位国内外院士，16篇论文曾入选ESI热点论文或高被引论文，施引单位遍布80多个国家。他多次入选《年度全球前2％顶尖科学家榜单》的“终身科学影响力排行榜”和“年度科学影响力排行榜”。
Intelligent Perception and Autonomous Avoidance of Spacecraft Confronting Orbital Threats
邱剑彬 教授
哈尔滨工业大学，中国
摘要
Spacecraft are high-value strategic assets, and their safe and stable in-orbit operation is the fundamental prerequisite for completing various tasks. Currently, the orbital space is becoming increasingly congested, with a sharp increase in collision risks. Space competition is intensifying, harassment is on the rise, and the number of threats continues to grow. The safe operation of spacecraft is facing severe challenges. At present, the orbital threats response measures heavily rely on ground support, featuring poor timeliness in handling threats and high operational control pressure, making it difficult to adapt to the deteriorating space situation. Therefore, it is urgent to develop the spacecraft’s autonomous avoidance ability against orbital threats and develop an integrated intelligent autonomous control system for orbital threat avoidance with an on-board closed-loop of “perception-decision-control”. This talk will introduce our research progress in recent years on the logical architecture analysis, modeling, and mission planning of the integrated control system for intelligent perception and autonomous avoidance of spacecraft confronting orbital threats.
个人简介
邱剑彬，哈尔滨工业大学航天学院教授/博士生导师，长期致力于智能控制理论与应用研究，获省部级自然科学一等奖4项、科技进步特等奖1项。入选国家级高层次人才、IEEE Fellow、全球高被引学者、德国洪堡学者等，担任《IEEE模糊系统汇刊》、《IEEE控制论汇刊》、《IEEE工业信息学汇刊》等国际著名期刊编委。