报告时间：11月21号 8:30-17:30
报告地点：宝山校区机自大楼322伯时会堂
柴利
武汉科技大学
个人简介：柴利，教授、博士生导师、国家杰出青年科学基金获得者，武汉科技大学人工智能与信息融合研究院经理、冶金自动化与检测技术教育部工程研究中心主任、首批“全国高校黄大年式教师团队”负责人。
柴利的主要研究兴趣为滤波器组框架理论及其在图像/视频处理中的应用、分布式优化、网络化控制系统等。在国际知名期刊和会议发表论文80余篇，主持完成五项国家自然科学基金项目，入选教育部新世纪优秀人才支持计划、湖北省新世纪人才计划等。曾至哈佛大学访问1年。现为中国疏浚协会信息与智能专业委员会副主任委员、湖北省自动化学会常务理事、湖北省人工智能学会常务理事。
报告题目：多智能体系统一致性控制的图频域方法
报告摘要：多智能体的一致性分析是多智能体网络优化设计中的重要科学问题之一，在多机器人协作、分布式优化计算、智能电网等领域有广泛应用。当前大部分的研究工作主要采用稳定性理论和Lyapunov泛函等方法来分析和设计网络的一致算法或控制策略，缺乏一致性收敛率与网络拓扑之间的直接联系。给定一类网络拓扑，不但无法设计达到最快收敛率的一致性控制策略，所能达到的最快一致性收敛率也是未知的。
本报告将从图频域滤波的角度揭示多智能体系统平均一致收敛的物理本质，建立分布式平均一致控制器、一致性收敛率与图频域滤波器之间的显式关系。在此基础上，针对未知拓扑网络一致性问题，我们将给出达到最快一致收敛的控制器的解析表达式及其实现方法，并给出最优一致收敛率上限的显式表达式。最后，我们通过多个仿真例子验证了所提算法的有效性。
付俊
东北大学
个人简介：付俊，东北大学流程工业国家重点实验室教授，博士生导师，长江学者特聘教授，国家杰出青年基金获得者，东北大学工业人工智能研究院副经理。2006年获东北大学控制理论与控制工程博士学位，2009年获加拿大康考迪亚（Concordia)大学机械与工程第二博士学位, 2010年-2014年为美国麻省理工学院（MIT）全职博士后研究员。荣获2016年中国自动化学会青年科学家奖，2018年教育部青年科学奖（为自动化领域首位获奖者）。主要学术兼职有：基金委信息科学部咨询专家委员会学术秘书，中国自动化学会副秘书长, IEEE Trans. Neural Networks and Learning Systems, IEEE Trans. on SMC: Systems, Control Engineering Practice, Journal of Industrial and Management Optimization、《自动化学报》中英文版和《系统工程学报》的编委。主要研究方向有动态优化、非线性控制、流程工业运行优化控制、机器人系统控制、工业人工智能等。
报告题目：混合智能动态优化与非线性控制
报告摘要：报告首先将介绍一个全新的可以在有限多次迭代下能够保证精确满足路径约束的动态非凸优化的理论框架，其次介绍非线性动态系统的切换控制和构造性控制方面所提出的几个新方法及其应用，最后对人工智能驱动的自动化和混合智能优化将来的研究工作进行展望。
莫一林
清华大学
个人简介：Yilin Mo is an Associate Professor in the Department of Automation, Tsinghua University. He received his Ph.D. In Electrical and Computer Engineering from Carnegie Mellon University in 2012 and his Bachelor of Engineering degree from Department of Automation, Tsinghua University in 2007. Prior to his current position, he was a postdoctoral scholar at Carnegie Mellon University in 2013 and California Institute of Technology from 2013 to 2015. He held an assistant professor position in the School of Electrical and Electronic Engineering at Nanyang Technological University from 2015 to 2018. His research interests include secure control systems and networked control systems, with applications in sensor networks and power grids.
报告题目：Secure Information Fusion in Cyber-Physical Systems
报告摘要：The concept of Cyber-Physical System (CPS) refers to the embedding of sensing, communication, control and computation into the physical spaces. Today, CPSs can be found in areas as diverse as aerospace, automotive, chemical process control, civil infrastructure, energy, health-care, manufacturing and transportation, most of which are safety critical. Any successful attack to such kind of systems can cause major disruptions, leading to great economic losses and may even endanger human lives. The first-ever CPS malware (called Stuxnet) was found in July 2010 and has raised significant concerns about CPS security. In this talk we discuss how to design secure and efficient information fusion algorithms for CPS. We consider the binary hypothesis testing problem with multiple sensors and design secure algorithm against an unknown set of Byzantine sensors. We further quantify the cost of adding security to the system and prove that our algorithm causes minimum impact on the performance in the absence of an attack.
史大威
北京理工大学
个人简介：2008年本科毕业于北京理工大学，2014年博士毕业于加拿大阿尔伯塔大学，2017-2018年在美国哈佛大学开展博士后研究工作，现任北京理工大学教授、博士生导师。主要从事复杂系统采样控制理论及在生物医学、机器人和工业过程中的应用研究。研究工作已在Automatica, IEEE Trans. on Automatic Control，IEEE Trans. on Control Systems Technology, IEEE Trans. on Biomedical Engineering等国际期刊上发表论文30余篇，获批美国发明专利及PCT国际专利2项，以第一作者在Springer出版英文专著1部。担任国际期刊IET Control Theory & Applications编委，European Journal of Control客座编委，IEEE Control Systems Society Conference Editorial Board成员，美国Mathematical Reviews评论员，入选2016年Automatica杰出审稿人。
报告题目：事件触发状态估计：从最优性到鲁棒性
报告摘要：本文主要讨论事件触发最优与鲁棒状态估计问题，探讨基于事件触发测量信息的低计算复杂度状态估计器优化设计方法。我们将首先考虑最小二次均方误差估计问题，讨论不同事件触发条件下最优或近似最优事件触发估计器的设计结果，并简要分析网络丢包对估计器设计的影响。接着我们将讨论事件触发鲁棒最优估计问题，介绍如何在风险敏感框架下实现兼顾鲁棒性和稳定性的、具有简单递推结构的事件触发估计器设计方法。此外，我们也将简要汇报部分估计方法在运动控制系统中的实验效果。
虞文武
东南大学
个人简介：虞文武，1982年生，2004年和2007年分别在东南大学获得学士和硕士学位，2010年在香港城市大学电子工程系获得博士学位。东南大学教授，数学、网络空间安全、控制科学与工程、统计学等学科研究生导师；江苏省网络群体智能重点实验室常务副主任、复杂系统与网络科学研究中心副主任、网络空间安全学院复杂网络应用与安全研究中心主任；入选国家“万人计划”青年拔尖人才、教育部“长江奖励计划”青年学者、国家优秀青年科学基金获得者；2014-2018连续五次入选科睿唯安/原汤森路透全球高引科学家（工程学）。
主要从事网络群体智能分析、控制、优化及其应用（复杂网络与复杂系统、多智能体系统、神经网络、网络系统控制与优化、网络智能与安全控制、无人系统、智能电网、智能交通、物联网与智慧城市、大数据分析）等相关研究，Springer合编书和Wiley专著各1部，发表SCI文章100余篇，其中IEEE汇刊、Automatica、SIAM杂志90余篇；Google引用过万次，SCI他引7000余次，SCI H指数47；32篇ESI高被引论文(学科前1%)。
担任IEEE Trans. Industrial Informatics、IEEE Trans. Systems, Man, and Cybernetics: Systems、IEEE Trans. Circuits and Systems II、中国科学信息科学和中国科学技术科学等SCI杂志编委；曾获国家自然科学二等奖1项（排名第2），省部级二等奖以上3项（1项排名第1）及国家一级学会科学技术奖一等奖1项（排名第1）、亚洲控制会议最佳论文奖等6篇国内外学术会议和机构论文奖。
报告题目：Machine Learning with Its Applications in Intelligent Transportation System
报告摘要：In this talk, I will address applications of machine learning techniques to intelligent transportation systems test cases. Intelligent transportation systems cannot be merely managed by human operators, as humans cannot control complex situations effectively. Therefore, intelligent control and monitoring methods are urgent, where "intelligence” is required to modify internal characteristics to adapt to the changes in the dynamics or in the environment. Three relevant test cases will be considered, where the need for such "intelligence” is crucial: use of adaptive dynamic programming tools to optimize traffic light signalling in urban traffic networks and adapt such strategies to changing traffic conditions; use of broad learning tools as a computationally efficient alternative to deep learning for traffic flow prediction, and as a way to fast reconfigure the prediction when new data arrive; use of self-learning adaptive control tools to establish platoons of vehicles in the presence of uncertainty in the vehicle/engine parameters.
何建平
上海交通大学
个人简介：Jianping He (M’15) is currently an associate professor in the Department of Automation at Shanghai Jiao Tong University. He received the Ph.D. degree in control science and engineering from Zhejiang University, Hangzhou, China, in 2013. His research interests mainly include the distributed learning, control and optimization, security and privacy in network systems. He was the winner of Outstanding Thesis Award, Chinese Association of Automation, 2015. He received the best paper award from IEEE WCSP’17, the best conference papers award from IEEE PESGM’17, and the finalist best student paper award from IEEE ICCA’17. He is the recipient of China National Recruitment Program of 1000 Talented Young Scholars.
报告题目： Learning and Attack via External Observation for Multi-Robot Systems
报告摘要：In this talk, we propose a statistical learning framework for multi-robot systems to infer the underlying interaction relations by observing trajectories of interacting robots. In our work, correlation algorithm adopted in Vector Auto-Regression (VAR) or diffusion model is improved and a transfer matrix estimator involving regression mechanism is proposed. In our newly proposed regression-based approach, the inference problem is converted to an empirical error function minimizing problem. We demonstrate the effectiveness of our approaches by providing theoretical guarantees and testing it on multi-robot rendezvous, formation and swarming systems. Besides, considering the link failure and creation, time slicing, dynamic window and graph mapping solutions are proposed for retrieving the dynamic interaction topology. Simulation results demonstrate the topology learning accuracy 95% in static interaction case and 90% in dynamic case. Small scale of experiments verify the feasibility of the scheme.
袁烨
华中科技大学
个人简介：Ye Yuan received the B.Eng. degree from the Department of Automation, Shanghai Jiao Tong University, Shanghai, China, in 2008, and the M.Phil. and Ph.D. degrees from the Department of Engineering, University of Cambridge, Cambridge, U.K., in 2009 and 2012, respectively. He has been a Full Professor at the Huazhong University of Science and Technology since 2016. He was a Postdoctoral Researcher at UC Berkeley, a Junior Research Fellow at Darwin College, University of Cambridge, and has been holding visiting researcher positions at California Institute of Technology, Massachusetts Institute of Technology, and Imperial College London. His research interests include system identification and control with applications to cyber- physical systems. Dr. Yuan has received the China National Recruitment Program of 1000 Talented Young Scholars, the Dorothy Hodgkin Postgraduate Awards, Microsoft Research Ph.D. Scholarship and Best of the Best Paper Award at the IEEE PES General Meeting 2017.
报告题目：Data-driven Discovery of Cyber-Physical Systems
报告摘要：A major cross-disciplinary challenge concerns the need to adequately model cyber-physical systems (CPSs). CPSs, which embed software into the physical world (for example, in smart grids, robotics, intelligent manufacture and medical monitoring), have proved resistant to modeling due to the intrinsic complexity arising from (a) the combination of physical and cyber components and (b) the interaction between systems. This study proposes a solution in the form of a general framework for reverse engineering CPSs from data without prior knowledge. The method, which draws from artificial intelligence, involves the identification of physical systems as well as the inference of computer logics using sparse identification. The novel framework, which has been applied successfully to a number of real-world examples, seeks to enable researchers to make predictions concerning the trajectory of CPSs based on the discovered model. Such information may prove essential for the assessment of the performance of CPS and the design of failure-proof CPS. We can also use the proposed framework for the creation of design guidelines for new CPSs.
吴均峰
浙江大学
个人简介：吴均峰，浙江大学控制科学与工程学院“百人计划”特聘研究员，博士生导师。2017年6月，加入浙江大学工业控制研究所孙优贤院士课题组。研究领域包括网络控制系统、信息物理融合系统、卡尔曼滤波、状态估计、多智能体系统、分布式优化算法设计与性能分析等。2005年-2017年，吴均峰博士曾先后在浙江大学控制科学与工程系、香港科技大学电子及计算机学系、瑞典皇家工学院(KTH)学习和工作。在网络控制领域围绕状态估计、卡尔曼滤波、多智能体系统等方面取得了一定研究成果。近五年来，先后在IEEE Trans. Automatic Control、Automatica、IEEE Trans. Signal Processing等国际期刊发表和录用论文30余篇。2015年获第34届中国控制会议关肇直奖，2016年获澳大利亚政府Endeavour Research Fellowship，分别获2014-2015年度Automatica与IEEE Trans. Control of Network Systems期刊杰出审稿人称号。曾担任IET Control Theory & Applications期刊专刊客座编辑（Leading Guest Editor）。
报告题目：Boolean Gossip Networks
报告摘要：We propose and investigate a Boolean gossip model as a simplified but non-trivial probabilistic Boolean network. With positive node interactions, in view of standard theories from Markov chains, we prove that the node states asymptotically converge to an agreement at a binary random variable, whose distribution is characterized for large-scale networks by mean-field approximation. Using combinatorial analysis, we also successfully count the number of communication classes of the positive Boolean network explicitly in terms of the topology of the underlying interaction graph, where remarkably minor variation in local structures can drastically change the number of network communication classes. With general Boolean interaction rules, emergence of absorbing network Boolean dynamics is shown to be determined by the network structure with necessary and sufficient conditions established regarding when the Boolean gossip process defines absorbing Markov chains. Particularly, it is shown that for the majority of the Boolean interaction rules, except for nine possible nonempty sets of binary Boolean functions, whether the induced chain is absorbing has nothing to do with the topology of the underlying interaction graph, as long as connectivity is assumed.
苏厚胜
华中科技大学
个人简介：华中科技大学人工智能与自动化学院教授、博士生导师。2008年12月毕业于上海交通大学自动化系获工学博士学位；2008年12月至2010年1月在香港城市大学电子工程系博士后；多次在香港大学机械工程系作为高级访问学者从事合作研究工作。获2015年国家自然科学二等奖（排3）、2014年教育部自然科学一等奖（排2）、2019年湖北省自然科学一等奖（排2）、2018年科睿唯安全球高被引科学家、教育部新世纪优秀人才、湖北省杰出青年基金获得者、华中学者特聘岗、湖北省优秀硕士和学士学位论文指导教师、美国大员工数学建模大赛一等奖指导教师、华中科技大学学术前沿青年团队负责人、华中科技大学学术新人奖、上海市优秀博士论文奖、上海图书奖二等奖。作为项目负责人主持国家自然科学基金4项，教育部博士点基金和湖北省自然科学基金等多项科研项目。发表SCI期刊论文120多篇，其中ESI高被引论文23篇、IEEE汇刊和Automatica论文40多篇。担任国际期刊IET Control Theory & Applications的Associate Editor。
报告题目：正性约束下的复杂网络连边一致性问题研究
报告摘要：绝大多数已有的复杂网络控制方面的研究都是基于网络的节点状态，然而对于某些侧重信息、数据或物质传输的网络来说，连边上的状态具有更重要的研究意义。本报告以基于连边动力学模型的网络化多自主体系统为研究对象，研究其连边一致性控制问题。主要介绍正性约束下的复杂网络连边一致性控制，包括：连续时间连边一致研究基础、基于状态反馈的连边正一致、参数确定或参数不确定下的基于观测器方法的连续连边正一致、离散时间连边正一致。
杨涛
东北大学
个人简介：杨涛博士2012年获美国华盛顿州立大学博士学位。2012年至2014年，在瑞典皇家理工学院任职博士后，2014年，加入美国能源部太平洋西北国家实验室，任职博士后，后晋升为科学家/工程师。2016年-2019年，在美国北得克萨斯州大学任职助理教授，2019年入选国家青年高层次人才类项目，加入东北大学流程工业综合自动化国家重点实验室，任职教授、博士生导师。 杨涛博士长期从事分布式协同控制和优化及其在能源互联网中的应用的研究工作。在国际重要期刊和学术会议上发表SCI/EI检索论文60余篇，其中IEEE汇刊和IFAC汇刊论文20余篇。多次在国际学术会议组织邀请组和会前专题研讨会，担任Annual Reviews in Control和IET Control Theory and Applications的客座编委，多个国际学术会议程序委员会委员，IEEE控制系统协会“网络和通信系统技术委员会”、“智能电网技术委员会”和“非线性系统技术委员会”委员。获美国橡树岭大学联盟Ralph E. Powe青年教授奖(2018年）和第14届IEEE控制与自动化国际会议上最佳员工论文奖（导师）。
报告题目：Distributed Optimization and its Applications in Power Systems
报告摘要：In the first part of this talk, we will review the existing distributed optimization algorithms, including the ones with diminishing step-sizes and fixed step-sizes. In the second part of this talk, we consider the optimal coordination problem for distributed energy resources (DERs) including distributed generators and energy storages. We develop two DER coordination algorithms to solve the optimal DER coordination problem in a distributed manner. In the proposed algorithm, each DER only maintains a set of variables and updates them through information exchange with a few neighboring DERs over a time-varying directed communication network. We show that the proposed distributed algorithm with appropriately chosen diminishing step-sizes solves the optimal DER coordination problem if the time-varying directed communication network is uniformly jointly strongly connected. Moreover, in order to improve the convergence speed and to reduce the communication burden, we propose an accelerated distributed algorithm with a fixed step-size. We show that the new proposed algorithm exponentially solves the optimal DER coordination problem if the cost functions satisfy an additional assumption and the selected step-size is less than a certain critical value. Both proposed distributed algorithms are validated and evaluated using the IEEE 39-bus system.
沈超
西安交通大学
个人简介：沈超，西安交通大学自动化学院教授/博士生导师，网络空间安全学院副经理，国家优秀青年科学基金获得者，达摩院青橙奖获得者，教育部学术新人，陕西省青年科技新星。目前主要从事数据驱动安全、信息物理融合系统安全、人工智能安全、大规模社交网络安全的研究工作。近年来承担并参与了国家自然科学基金、创新群体、重点研发计划、预研重点基金以及部委与企业项目20余项。研究成果发表论文50余篇，包括USENIX Security, ACM CCS, Automatica, IEEE TFS, IEEE TC, IEEE TSMC, IEEE TDSC, IEEE TIFS等期刊和会议；获得教育部自然科学二等奖1项，7次国内外学术会议最佳/优秀论文的奖励；主持和参与研制了多个重要系统并应用于国家大型企事业单位。担任多个国内外期刊（3个为JCR一区）副编辑或编委，以及数十个国内外学术会议组织委员会或程序委员会成员。
报告题目：信息物理融合的智能电网安全分析
报告摘要：近年来国际上爆发了多起恶意攻击导致的电网重大安全事故，包括2015年、2016年的乌克兰大面积停电以及2019年的委内瑞拉全国性大停电等，使国家安全受到严重威胁并给人民生产生活带来了巨大的经济损失。智能电网由于电气元件与计算信息在网络空间中深度耦合，易遭受基于信息物理融合的恶意攻击破坏。本报告简述现有针对智能电网的攻击特性并分析攻击对智能电网的安全影响，探讨结合数据驱动和电网潮流机理分析的智能电网攻击检测、脆弱性分析以及主动防御方法，本报告还将讨论其它面向信息物理系统的安全问题。同时希望通过本报告能够进一步揭示智能电网对于恶意攻击的脆弱性，为电网操作者的系统安全规划和保护措施设计带来启发。
李渝哲
东北大学
个人简介：李渝哲，男，目前为东北大学流程工业综合自动化国家重点实验室教授，博士生导师，中组部“青年千人”。2011年8月本科毕业于北京大学工学院力学系，获学士学位，同时获得北京大学经济学双学士学位。随后进入香港科技大学电子与计算机工程学系，并于2015年8月获得博士学位。曾先后在澳大利亚纽卡斯尔大学、加拿大阿尔伯塔大学、香港中文大学进行访问和研究工作。2018年6月加入东北大学流程工业综合自动化国家重点实验室柴天佑院士课题组。主要研究方向包括网络化系统的状态估计、控制与优化，信息物理系统的安全与隐私等，作为负责人主持基金委重大项目课题在内的多项科研项目。
报告题目：Cyber-Physical Security in Remote State Estimation
报告摘要：Cyber-Physical Systems (CPS) have attracted considerable interest from both academic and industrial communities in the past few years. Using wireless sensors for remote state estimation is a key component in CPS, and have advantages such as low cost, easy installation, and self-power. However, due to the inherent open characteristics of wireless communication, and the increasing penetration of CPS to safety-critical infrastructures of the society, cyber-physical security issues arise naturally and are of fundamental importance to ensure the safe operation of CPS. In this talk, our recent results about the cyber-physical security in remote state estimation will be introduced.
